International Hydrological Programme

Satellite Remote Sensing ofAtmospheric Constituents-The Eighteenth IHP Training Course in 2008-

Edited by Hirohiko Masunaga

Hydorospheric Atmospheric Research Center, Nagoya University

United Nations Educational Scientific and Cultural Organization

The Eighteenth IHP Training Course(International Hydrological Program)Satellite Remote Sensing of Atmospheric Constituents

3-15 November 2008

Nagoya, Japan

Working Group for IHP Training Course,
Sub-committee for IHP
Japanese National Commission for UNESCO

Outline
As a part of the Japanese contribution to the International Hydrological Program (IHP), a
short course on satellite remote sensing of atmospheric constituents will be conducted forparticipants from the Asia-Pacific regions. The course will be held for the period from 3 to 15
November 2008 at Hydrospheric Atmospheric Research Center (HyARC) and Information
Technology Center, Nagoya University, Nagoya, Japan. The course includes a series of lectures inEnglish, practical sessions, and technical tours.
Objectives
Satellite remote sensing is a unique tool to homogeneously observe our whole planet. A
variety of meteorological satellites are currently in operation to monitor clouds, precipitation,
aerosols, and different gaseous components of the atmosphere. The applicability of satellite data
ranges over a broad area from operational use to climate studies. The 18th IHP training course will
provide an opportunity for participants to learn the fundamentals of satellite remote sensing and its
application to atmospheric sciences. The training course offers introductory lectures on the basics ofmeteorological satellite observations and the physical principles of retrieval algorithms. Also offeredis a practical training course for the participants to establish basic skill to analyze satellite data.

Course Contents (convener: H. Masunaga)
Lecturers
Aonashi, Kazumasa (Meteorological Research Institute, Japan Meteorological Agency)
Higuchi, Atsushi (Center for Environmental Remote Sensing, Chiba University)
Iguchi, Toshio (Applied Electromagnetic Research Center, National Institute of Information andCommunications Technology)
Kachi, Misako (Earth Observation Research Center, Japan Aerospace Exploration Agency)
Kawamoto, Kazuaki (Faculty of Environmental Studies, Nagasaki University)
Kubota, Takuji (Earth Observation Research Center, Japan Aerospace Exploration Agency)
Masunaga, Hirohiko (Hydrospheric Atmospheric Research Center, Nagoya University)
Nakamura, Kenji (Hydrospheric Atmospheric Research Center, Nagoya University)
Okamoto, Hajime (Center for Atmospheric and Oceanic Studies, Tohoku University)
Okamoto, Kozo (Forecast Department, Japan Meteorological Agency)
Sano, Itaru (School of Science and Engineering, Kinki University)
Ushio, Tomoo (Graduate School of Engineering, Osaka University)
Yamamoto, Munehisa K. (Center for Environmental Remote Sensing, Chiba University)
Yokota, Tatsuya (Center for Global Environmental Research, National Institute of Environmental
Studies)Lectures
L0 Introduction- Guidance and outline of the IHP short course

H. Masunaga

L1 Overview of Meteorological Satellites in Orbit
H. Masunaga- A brief summary of satellites and sensors being used for meteorological research and weather
forecasting.
L2 Satellite Remote Sensing of Clouds by Passive Sensors- Methodologies to retrieve cloud physical properties from space- Applications to climate studies

K. Kawamoto

L3 Satellite Remote Sensing of Clouds by Active Sensors- Cloud profiling by ground-based and spaceborne W-band radars- Current and future satellite missions carrying cloud profiling radars

H. Okamoto

L4 Satellite Remote Sensing of Aerosols- Methodologies to retrieve aerosol optical properties from space- Applications to climate and environmental studies

I. SanoL5 Satellite Remote Sensing by Temperature and Humidity Sounders- Methodologies of atmospheric sounding from space- Application to data assimilation

K. Okamoto

L6 Geostationary Meteorological Satellites
A. Higuchi- Introduction to existing geostationary satellites for meteorological applications- Cloud and water-vapor monitoring by GMS/MTSAT: physical principles and applications
L7 Tropical Rainfall Measuring Mission (TRMM)- Mission outline and precipitation data products available- Applications to climate studies and extreme weather monitoring

K. Nakamura

L8 Space borne Precipitation Radars- Rainfall profiling by space borne K-band radars- Current and future satellite missions carrying precipitation radars

T. Iguchi

L9 Precipitation Retrieval by Microwave Radiometers- Methodologies to retrieve precipitation from space- Applications to data assimilation

K. Aonashi

L10 Global Satellite Mapping of Precipitation (GSMaP)- Introduction to the GSMaP project

T. Ushio-Retrieval algorithms and data products

L11 Greenhouse Gas Observing Satellite (GOSAT)- Introductions to the GOSAT project- Retrieval methodology and expected outcome.

T. Yokota

Practices
P1 Geostationary Meteorological Satellite (GMS) Data Analysis
M. K. Yamamoto- On-site computer exercise to analyze GMS data
P2 Real-time Global Precipitation Data Analysis
T. Kubota- On-site computer exercise to analyze Global Satellite Mapping of Precipitation (GSMaP) data
Technical Tours
Japan Aerospace Exploration Agency in Tsukuba- Guided tour
- Seminar for satellite data/images access through JAXA EORC WWW site

M. KachiSchedule (3-15 November 2008)
3 (Monday)

Arrival at Nagoya

Nagoya4 (Tuesday)

10:00-12:00: Guidance (Lecture 0) and Lecture 1 by H. Nagoya
Masunaga
14:00-16:00: Lecture 2 by K. Kawamoto
Reception at Nagoya University in the evening

5 (Wednesday)

10:00-12:00: Lecture 3 by H. Okamoto

Nagoya14:00-16:00: Lecture 4 by I. Sano
6 (Thursday)

10:00-12:00 Lecture 5 by K. Okamoto

Nagoya14:00-16:00 Lecture 6 by A. Higuchi
7 (Friday)

10:00-12:00: Practice 1 supervised by M. K. Yamamoto

Nagoya14:00-16:00: Practice 1 (contd.)
8 (Saturday)

Japanese culture introduction and free time

Nagoya9 (Sunday)

Technical Tour: Japan Aerospace Exploration Agency Tsukuba(JAXA) (Move to Tsukuba): Guided tour

10 (Monday)

Technical Tour (contd.): JAXA EORC seminar by M. Kachi Tsukuba
Move back to Nagoya

11 (Tuesday)

10:00-12:00: Lecture 7 by K. Nakamura

Nagoya14:00-16:00: Lecture 8 by T. Iguchi
12 (Wednesday)

10:00-12:00: Lecture 9 by K. Aonashi

Nagoya14:00-16:00: Lecture 10 by T. Ushio
13 (Thursday)

10:00-12:00: Practice 2 supervised by T. Kubota

Nagoya14:00-16:00: Practice 2 (contd.)14 (Friday)

10:00-12:00: Lecture 11 by T. Yokota

Nagoya14:00-: Closing Ceremony
15 (Saturday)

Departure from Nagoya

 All lectures are held at the lecture room (3F) of the Hydrospheric Atmospheric Research Center,
Nagoya University.
 Practices are held at the Information Technology Center, Nagoya University.

Lecture 1

Introduction and Overview of Meteorological Satellites in Orbit
Hirohiko Masunaga
Hydrospheric Atmospheric Research Center, Nagoya University
This opening lecture overviews the Earth observing satellites currently being operated for
meteorological applications. Meteorological satellites may be classified into distinct categories byorbital configuration. Geostationary satellites, phase-locked with the Earth rotation, provide
temporally continuous observation on a broad area facing the satellite. The low-Earth orbiting (LEO)
satellites, in contrast, typically fly at altitudes of a few hundred to a thousand kilometers. LEO
satellites carry a number of different sensors, including microwave radiometers and radars, that are
not onboard the geostationary satellites. Governmental agencies operating these satellites may be
divided into “operational agencies” and “space agencies”. The former is expected to provide
continuous, stable services, while the latter develops satellites carrying technologically advanced
sensors. Geostationary satellites, instrumental for daily weather monitoring, are typically operated by
the operational agencies. Space-borne radars, in contrast, are relatively experimental for use by theweather bureaus and operated by the space organizations such as the Japan Aerospace Exploration
Agencies (JAXA) and U.S. National Aeronautics and Space Administration (NASA).
Lectures in the training course are focused on a variety of satellite capabilities available to date.
Physical principles to measure cloud properties from satellite remote sensing, with both traditionaland newest technologies, are introduced (L2 and L3). Aerosol measurements are also important formonitoring and assessing air pollution and the aerosol impact on climate (L4). Sounding air
temperature and humidity is crucial for numerical weather prediction (L5). Geostationary satellites
are particularly useful for cloud and water vapor observations (L6). The second week of the training
course starts with an introduction to the Tropical Rainfall Measuring Mission (TRMM) (L7), which
is a recent technological progress in measuring global precipitation with the Precipitation Radar
aboard (L8). Global precipitation estimates are now conveniently organized into user-friendly
datasets by dedicated efforts across the world (L9). Finally, global monitoring of greenhouse gases
by a newest satellite, to be launched just in a few months, is reviewed (L10).

Lecture 2

Satellite Remote Sensing of Clouds by Passive Sensors
Kazuaki Kawamoto
Faculty of Environmental Studies, Nagasaki University

This lecture describes a satellite remote sensing investigation with passive sensors, aiming following
two points: 1) a methodology to retrieve cloud properties and 2) an extension to climate studies. To
begin with, the basic conception of remote sensing is given by some examples such as the dragon
and its tracks, and forward and backward problems. A target cloud type in this lecture is low-level
water cloud, since radiative properties of non-spherical ice particles are still difficult to apply. After
introducing aerosols and cloud properties such as optical depth and representative particle size,
principles are explained in detail to retrieve the optical depth, effective particle radius and top
temperature of clouds using visible (0.6m), near-infrared (3.7m), and thermal-infrared (11m)
spectral channels. A brief history of cloud remote sensing is also presented. Then geographicaldistributions of cloud properties on global and regional scales are shown, applying above principles
to data from passive satellite sensors. Land-ocean contrasts and vertical profiles of cloud propertiesare consistent with cloud condensation nuclei (CCN) number density, as the Twomey effect suggests.
Cloud properties thus obtained are compared with the aerosol abundance in East Asia. When
aerosols increase, the optical depth and particle number density of clouds also increase, but the cloud
effective particle radius decrease. These behaviors are understood by the Twomey effect. Finallyrelationships of cloud properties with the precipitation amount are examined. Analyses reveal thatprecipitation could be one of major controllers of cloud and aerosol properties. For future strategy,
passive remote sensing should take synergetic uses with active remote sensing and numericalmodels.

Lecture 3

Satellite remote sensing of clouds by active sensors
Hajime Okamoto
Center for Atmospheric and Oceanic Studies, Graduate School of Science,
Tohoku University
In this lecture, I introduce the satellite and ground-based remote sensing of clouds (and aerosols).
The lecture consists of 8 chapters. (1) what is active remote sensing, (2) About scattering andabsorption by particles, (3) Lidar and radar equations, (4) why clouds are important, (5)Observations of clouds from radar and lidar, (6) Evaluation of the Clouds from the Atmosphere
General Circulation Model, (7) Results from CloudSat and CALIPSO data, (8) Future satellite
mission with radar. In chapter (1), I discuss about the differences between the widely used passive
sensors and active sensors. Some measurements of high cirrus clouds from ground based radar andlidar are shown. Some materials are taken from the new satellite mission CloudSat and CALIPSO. In
the next chapter, the basic concept about the extinction, scattering and absorption of particles are
introduced by using electric and magnetic fields inside and outside the particles. Relation between
pointing vectors and scattering cross sections are derived. Definition of backscattering coefficient isalso treated here since this is the most relevant parameter in the active remote sensing. And therelationship between these scattering properties and microphysical properties such as size
distribution, phase, refractive index, particle shapes are discussed. As a non-spherical particle
scattering theory, some details about the discrete dipole approximation (DDA) are explained. Thenlidar and radar equations are discussed. We might see the observables are finally related to themicrophysics of particles through these equations. These two equations are very similar except forthe dependence of single scattering properties on cloud microphysics. Basic features of lidar andradar are summarized. The differences in the features of the radar and lidar, may be easily
understood from the knowledge already shown in the previous chapter and these differences also
lead to the understanding of the basics of the particle sizing from the combination of these twodifferent sensors. I also provide some basic knowledge about the discussions related to issues in
the numerical modeling such as atmospheric general circulation models and concepts of cloud feedback processes. Then ship-based radar and lidar observations and results of analyses are discussed.
Evaluation of the representation of clouds is examined. There are some efforts to develop radar andlidar simulators, i..e, signals are estimated from the model output and sensitivity of instruments andobservational conditions are included. Results from CloudSat and CALIPSO are presented. And
finally, outline of new mission EarthCARE, and scientific targets are introduced.

Lecture 4

Aerosol Remote Sensing
Itaru Sano
Faculty of Science and Technology, Kinki University
Incident solar radiation interacts with aerosol particles in the atmosphere. Such a phenomenon by
aerosols as scattering and/or absorption of radiation directly affects Earth's radiation budget. The
atmospheric aerosols also play an indirect effect in the radiation budget as the cloud condensation
nuclei, because the cloud influences upon the radiation field and precipitation. Accordingly in orderto evaluate the Earth's radiation system, the precise aerosol information and its role is desired in the
globe scale.
This lecture focuses on the aerosol remote sensing from space. The idea of aerosol remotesensing is based on the fact that satellite has received the scattered light by the atmospheric particles.
In other word, the satellite data could involve the scattering characteristics of aerosols. Certainly thesurface properties should be considered for aerosol retrieval from satellite data. First, single
scattering pattern is calculated as: Lorenz-Mie and Rayleigh theory for aerosols and molecular gases,
respectively. Then multiple scattering problem is solved by using the adding-doubling method. It isenhanced that our calculation is made for semi-Stokes parameters {I,Q,U}, because polarization
information is available for our aerosol retrieval. Finally, the numerical results are stored in thelook-up tables. Then aerosol properties are retrieved in accordance with the optimized comparison ofsatellite data with the corresponding values in the look-up tables. In this lecture, the obtained global
distribution of aerosol properties and their seasonal change are shown.
The NASA/AERONET ground based aerosol measurements are used for validation for the
results of satellite aerosol retrieval. The federated sun photometer network (aerosol robotic network;
AERONET) provides the aerosol properties all over the world, which include optical thickness atmany wavelengths, refractive index of real and imaginary part at several wavelengths, volume size
distribution and other valuable properties. Three AERONET stations have been operated inside
Japan by myself collaborated with NASA/GSFC since 2000.

All data are distributed to theresearchers from the AERONET web (http://aeronet.gsfc.nasa.gov).
New generation aerosol sensors called JAXA/GCOM/SGLI are under developed. The reflectancedata in the near UV wavelength and polarization information in the red and NIR wavelengths to beobserved by SGLI sensor look promising for extracting aerosol information from space. Thesefeatures are very unique in comparison with other sensor as VIIRS/NPOESS mission etc.

Lecture 5

Satellite Remote Sensing by Temperature and Humidity Sounders
Kozo Okamoto
Numerical Prediction Division, Forecast Department,
Japan Meteorological Agency (JMA)
This lecture shows features, principles and applications of passive atmospheric sounders. Sounders
extract vertical distributions of temperature and atmospheric constituents such as humidity using a
number of wavelength with different radiative characteristics. They have been long and widely usedby operational and research communities. In particular, nadir-looking sounders in the infrared andmicrowave spectral regions are essential for data assimilation in numerical weather prediction(NWP) because of the ample information and wide coverage.
In the lecture, after the introduction of typical instruments and data examples, what defines a shape(peak height and sharpness) of “weighting function” and how to retrieve atmospheric parametersfrom radiances sounders measure are discussed. While various retrieval products are used in anenvironmental monitor, process study and, validation of models and other observations, recent NWP
systems directly assimilate radiances. The direct assimilation more effectively and properly extracts
observation information although it requires careful data pre-processing, including quality check andbias correction, and accurate radiative transfer models. The performance and pre-processings of the
JMA’s operational system are given in the lecture.

Lecture 6

Geostationary Meteorological Satellites
Atsushi Higuchi
Center for Environmental Remote Sensing, Chiba University
This lecture focused on the Geostationary Meteorological Satellites (hereafter GMSs) operated byoperational agencies. As well-known, the Earth images captured by GMSs widely apply for daily
weather monitoring mainly via TV news or on website. GMSs are operated as phase-locked with
the Earth rotation, thus biggest advantage of GMSs is high temporal observation with wide area
coverage. Basic observation wavelengths derived from GMSs are consist of: One or two bands inthermal-infrared (IR), corresponding to atmosphere's window wavelength (10-13 micron), water
vapor band of 7 micron, some of GMSs have middle-IR band for the water droplet in clouds (3micron), and visible band. Currently, at least the six GMSs cover the whole of globe under theframework of World Meteorological Organization (WMO).

Spacecrafts of GMSs are briefly

divided into two types by the scanning and orbit-control methodology. One is spin type (e.g.,
Meteosat, Meteosat Second Generation (MSG) both operated by EUMETSAT, GMS-5 operated
by JMA, FY2 Series by CMA). Spin type GMSs are rotate themselves for the observation andstabilization ofthem. The other isthree-axes control type, such as new generation GOES

operated by NOAA and MTSAT-1R (HIMAWARI-6) by JMA. In lecture time, I showed several
animations (consist of hourly images in each observation band) derived from MTSAT-1R, for easy
to understand how to visualize the feature of clouds by GMSs.
To describe and/or diagnose the feature of clouds from GMSs, several analyzing techniques areshown in this lecture. As example, simple method for the detection of convective activity isutilization of thermal-IR image. For the detection of low-level clouds, visible image is useful during
daytime. In addition, Hovmollor diagram, time-longitude section of thermal-IR slice cutting of target
latitude belt, is widely applied for the description of convective activity moving with time. Moreover,the combination technique of two or more band images are also widely applied for the field of
meteorology. For example, the split window technique of which estimates two thermal-IR bands
images difference, is used for the separation of cirrus and deep convective clouds. Such multi-bands
or channels applications are adapted in International Satellite Cloud Climatology Project (ISCCP)
product. At the end of this lecture, the activity in Center for Environmental Remote Sensing(CEReS),

collecting and publishing the major GMSs dataset under the framework of four

university's centers virtual laboratory formation (used this dataset in Practice 1) and inter-calibrationof GMSs (Global Space-based Inter-Calibration System: GSICS) were introduced.

Lecture 7

Tropical Rainfall Measuring Mission (TRMM)
Kenji Nakamura
Hydrospheric Atmospheric Research Center, Nagoya University
The Tropical Rainfall Measuring Mission (TRMM) is a joint satellite venture between Japan and theU. S. The satellite was launched in November 1997, and is still working in orbit. The TRMM
satellite is equipped with a rain radar (PR), a microwave radiometer (TMI), a visible/infrared
radiometer (VIS), and a lightning sensor (LIS). As the instruments say, TRMM is dedicated for
accurate rain measurement. Particularly, PR is a powerful instrument for observation of precipitation.
PR has been developed in Japan, and the first spaceborne rain radar. PR and TMI both apply
microwaves, and directly observe rain. The combination of PR and TMI has contributed much to theunderstanding of the precipitation features over tropical and subtropical regions.

Precipitation is one of the major players in the water cycle over the globe. Water evaporates fromocean or land surface, is transported by wind, and falls as the precipitation. In the global change, oneof the key issues is the change of the water cycle, particularly, rain. For this issue, the achievementsof TRMM is invaluable and include: much improved global rain distribution, latent heating profiles,
and improvement of the rain retrieval algorithms from space. For example, currently, a few hourly
near global rain distribution is generated using multi-satellite data with TRMM as a core data source.

Based on the science and engineering heritage of TRMM, a next generation satellite project has beenlaunched as the Global Rainfall Measurement (GPM). GPM aims to provide at least three-hourly
global rain distribution in near real time. GPM is a multi-satellite system with a core satellite as a
TRMM follow-on and with constellation satellites with microwave radiometers. One of the main andunique sensors is the dual-wavelength radar (DPR) onboard the core satellite. DPR is expected toprovide more accurate rain rate estimation and also make the solid precipitation retrieval possible.

Lecture 8

Space borne Precipitation Radars
Toshio Iguchi
National Institute of Information and Communications Technology
The lecture starts with an overview of the general principle of radar measurements of precipitation.
It then explains the special conditions in precipitation measurements from space, and how these
conditions result in peculiarities of satellite-borne radar. The major hardware constraints given to a
space-borne radar are the size, mass, and power consumption together with the reliability. The
observation conditions and geometry that are very different from a ground-based radar also constrain
the design of a space-borne radar. Some of the important factors in this regard are the distancefrom the radar to the target, observation angles, existence of Earth's surface behind rain, and the
motion of the platform. These factors affect the sensitivity, resolution, observable regions that are
free from surface clutter and possibility of Doppler measurements. One good example is the use of
relatively short wavelength of electromagnetic waves for the radar in order to realize a good
horizontal resolution with a limited size of the antenna. Since high frequency radio waves sufferfrom attenuation by precipitation itself and other elements in the atmosphere such as water vapor,clouds, and oxygen molecules, a data processing for attenuation correction becomes an essential partof the rain retrieval algorithm. High frequency radio waves also create non-Rayleigh scattering andmakes it difficult to estimate rain rates quantitatively. In the lecture, how these issues are handled
in the current algorithm developed for the Precipitation Radar onboard the TRMM satellite.

It willmention the importance of the drop size distribution information and the surface reference techniquefor attenuation correction. The lecture ends with an introduction of other current and future space
missions that carry satellite-borne radars for precipitation and cloud observation. They include the
Global Precipitation Measurement (GPM), CloudSat, EarthCARE, and ACE.

Lecture 9
Precipitation Retrieval by Microwave Radiometers
Kazumasa Aonashi
Meteorological Research Institute, Tsukuba, Japan

Heavy precipitation around meteorological disturbances often causes floods or other natural
disasters. Hence, precipitation monitoring is very important task for disaster prevention agencies.
Though, accurate precipitation observation data are sparse, especially over oceans.
As tools for global precipitation monitoring, satellite microwave radiometers have drawn attention
recently. This is mainly because satellite microwave radiometer data give direct signals fromhydrometers over wide ranges. This presentation describes the basic principles of the passive
microwave precipitation retrieval, and the Global Satellite Mapping of Precipitation project(GSMaP) passive microwave precipitation retrieval algorithm using TRMM Microwave Imager(TMI) brightness temperatures (TBs). The GSMaP algorithm employs Polarization Corrected
Temperature (PCT) at 37 and 85 GHz (PCT37, PCT85) over land and coast, TBs with verticalpolarization at 10, 19, and 37 GHz (TB10v, TB19V, and TB37v) in addition to PCT37 and PCT85
over ocean.
In order to validate our algorithm, we compared its retrievals from TMI with the Precipitation
Radar (PR) precipitation rates for some Baiu cases and global precipitation distribution in 1998. The
results show:
1) The over-land and over-coast retrievals agreed better with PR than the retrievals only using
PCT85, while the correlation degraded for precipitation heavier than 10 mm hr-1. The over-land andover-coast retrievals underestimated PR precipitation rates for precipitation heavier than 10 mm hr-1.
These errors were caused by the problems in the forward calculation of scattering signals at the highfrequencies. In order to alleviate this, we need to improve the precipitation-related variable models
and RTM program for frozen precipitation.
2) The over-ocean retrievals agreed well with PR, in spite of slight overestimation of precipitation
weaker than 10 mm hr-1. This can be due to the neglect of the slant-path effect.
3) Our algorithm overlooked some weak precipitation areas over sub-tropical oceans. This arosefrom the forward calculation of emission signals. In order to improve the over-ocean precipitation
detection method, we need to give more realistic humidity and CLWC for shallow precipitation
regions.
This presentation also reports our current work on the assimilation of satellite microwave TBs into
a cloud-resolving model.

Lecture 10

Global Satellite Mapping of Precipitation (GSMaP)
Tomoo Ushio
Department of information and communication engineering, Osaka University
This lecture overviews the Global Satellite Mapping of Precipitation (GSMaP) project with aparticular emphasis on a system which has been recently developed and implemented that integrates
passive microwave radiometer data with infrared radiometer data. In order to have high temporal(1 hour) and spatial (0.1 degree) resolution global precipitation estimates, the product(GSMaP_MVK) is produced based on a Kalman filter model that refines the precipitation rate
propagated based on the atmospheric moving vector derived from two successive IR images. The
method was introduced, evaluated and compared with other high-resolution precipitation products
and the ground-based data collected by the Automated Meteorological Data Acquisition System(AMeDAS) near Japan. It was clearly shown that the approach described in this lecture performed
better than without the Kalman filter, and the time series of the hourly global precipitation patterndemonstrated the potential capabilities for weather monitoring and typhoon tracking. The
GSMaP_MVK product achieved a score comparable to the CMORPH and the 3B42RT products.

The 18th IHP Training Course“Satellite Remote Sensing ofAtmospheric Constituents”
Hirohiko Masunaga
Hydrospheric Atmospheric Research Center, Nagoya University

Program Aims
 IHP Nagoya Training Course

A part of the Japanese contribution to the UNESCO
International Hydrological Program (IHP) Held every year at the Hydrospheric AtmosphericResearch Center (HyARC), Nagoya University.
 The 2008 (18th ) training course “Satellite Remote
Sensing of Atmospheric Constituents” is aimed to offer an
opportunity for participants to: Learn the fundamentals of satellite remote sensing.
 Earn the basic skill to analyze satellite data.



Lecturers, invited from across Japan, are all leading
researchers in the relevant fields.
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Logistics -1
 Overall schedule

Nov 3 (Mon): Arrival in Japan Nov 4 (Tue) – 6 (Thu): Lectures #1-6
 Nov 7 (Fri): Practice #1
 Nov 8 (Sat): (All day break) Nov 9 (Sun) – 10(Mon): Technical tour to Tsukuba
 Nov 11 (Tue) – 12 (Wed): Lectures #7-10
 Nov 13 (Thu): Practice #2
 Nov 14 (Fri): Lecture #11 and closing ceremony
 Nov 15 (Sat): Departure from Japan Each class is 2-hour long: morning class is 10:00-12:00
and afternoon class is 14:00-16:00.


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Logistics -2
 Venue

All lectures are given at HyARC lecture room (301).
 All practices are held at Information Technology Center(ITC).


HyARC (here)

ITC

The trainees will make a two-day visit to the Japan
Aerospace Exploration Agency (JAXA) in Tsukuba.
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Tsukuba

Tokyo
Nagoya18th IHP Training Course

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Logistics -3
 To be announced.

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Overview of Meteorological Satellites in Orbit

HIROHIKO MASUNAGA
Hydrospheric Atmospheric Research Center, Nagoya University

Atmospheric Constituents
 Gases

Water vapor: a key of global water cycle
 Greenhouse gases (CO2 etc.) Cloud

Cools the Earth’s surface by reflecting solar radiation.
 Warms the Earth’s surface by trapping longwave radiation.
 Precipitation

Provides water resource to all of us living on the Earth.
 Could causes devastating disaster.
 Aerosol

Soil dust, biomass carbon, anthropogenic pollutant, etc.
 A source of air pollution
 Could modify surface radiation flux in various ways.


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Global water cycle

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Why do we need satellites? -1
 Think globally, act globally!

Satellites observe the Earth thoroughly and homogeneously.
 The only tool to look into the vast majority of our planetthat is beyond the reach of ground-based observations.
 Disaster prevention

Watching for severe storms potentially causing flush flood.
 Tropical cyclone tracking.


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Why do we need satellites? -2
 Weather forecast

Data assimilation: satellite data
help numericalmodels with better weather
prediction.

 Climate monitoring

Impacts of cloud and aerosols on
the Earth’s radiation budget
 Long-term change to global watercycle
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Satellites at dawn Our planet is now surrounded by…

5,924 satellites (including those decayed)in orbit, as of May 14, 2007.
 25,468 debris(source: celestrak.com/satcat/boxscore.asp)

Growing in number by ~700/year

30,00020,00010,000~700/yr

Cataloged
Decayedcelestrak.com/satcat/growth.pdf

2008

1957

On Orbit© Harald Edens
www.weather-photography.com
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Meteorological satellites -Introduction
 Satellite categories – separated by orbital altitude

Geostationary satellites
 Orbiting at ~36,000 km in altitude above the equator.
 Constantly observe an area on the Earth facing the satellite.



Low-Earth orbiting (LEO) satellites
 Altitude ranges from a few hundred to a thousand km.

 Mostly sun-synchronous satellites (polar orbiting) Observe a given area on the same local time almost every day.

 Some are sun-asynchronous.
 TRMM etc.

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Satellite programs -1
Satellites maintained by operational agencies
Expected to provide continuous, stable services.
 Geostationary satellties

GMS (Geostationary Meteorological Satellite) or currently MTSAT (Multifunctional Transport Satellite)

GOES (Geostationary Operational Environmental Satellite)European Organisation for the Exploitation of Meteorological Satellites(EUMETSAT)

Fengyun

US National Oceanic and Atmospheric Administration (NOAA)

MeteosatJapan Meteorological Agency (JMA)

China

Low-Earth Orbiting (LEO) satellites


NPOESS (Natl Polar-orbiting Operational Environmental Satellite System)

The U.S. tri-agency program to integrate existing independent programs.

MetOp


EUMETSAT
18th IHP Training Course

11/4/2008

Satellite programs -2
 Satellites developed and operated by space agencies

Allowed to carry technologically advanced sensors.
 Currently in orbit TRMM (Tropical Rainfall Measuring Mission) National Aeronautics and Space Administration (NASA)/Japan

Aerospace Exploration Agency (JAXA)Terra, Aqua, and CloudSat
 NASA

Being planned
 GCOM (Glocal Climate Observing Mission)
 JAXA EarthCARE (Earth Cloud, Aerosol, and Radiation Explorer) JAXA/European Space Agency (ESA)

 GPM
 NASA/JAXA/…

18th IHP Training Course

11/4/2008

1975 1980 1985 1990 1995 2000 2005 2010 2015 2020
GOES(US)GMS(JP)MTSAT(JP)Meteosat(EU)NOAA (6+,US)DMSP(US)NPOESS(US)MetOp(EU)
Geostationary
LEO (in operation oroperation ceased）

Aqua(US)
Terra(US)LEO (planned)
ADEOS/ADEOS II(JP)Continuous operation
is expected in future.
Operation will cease
sometime in future.

TRMM(JP/US)
CloudSat(US)GCOM-W/C(JP)GPM(JP/US+)
EarthCARE(EU/JP)1975 1980 1985 1990 1995 2000
2010
2020
18th IHP2005
Training
Course 2015
11/4/2008

Satellite Remote Sensors
Passive Sensors

L9
Microwave Radiometers
L2 Visible/IR Imagers(AVHRR, Geo sensors,(SSM/I,TMI,AMSR etc.)
L4 POLDER etc.) L6 L10

IR Sounders
L5 Microwave Sounders(TOVS-HIRS, AIRS(AMSU etc.)
etc.)
VisibleLidars(CALIPSO etc.)

L3

VerticalProfiling

Microwave
L7
Radars
L8(TRMMPR,
Cloudsat-CPR, etc)

Active Sensors
18th IHP Training Course

11/4/2008

Passive versus Active sensors
 Passive remote sensors
Measure natural-origin radiation
 Imager, radiometers, and soundersObserve two-dimensional plan view.
 Except for sounders, which measure vertical profiles as well.



Relatively inexpensive

 Active remote sensors
Send electromagnetic pulses and detect the strength andtemporal delay of back-scattered signal.
 Radars and lidars
 c.f., scatterometers

Observe three dimensional structure Tend to be expensive

18th IHP Training Course

11/4/2008

Different bands for different targets
 Visible lightAtmospheric particle scattering Cloud and aerosol optical properties

 Infrared radiation

Cloud top temperature Molecular absorption Temperature and humidity sounding Carbon dioxide retrieval (GOSAT) Microwave
Sensitive to liquid water when particles are large
 Precipitation retrieval

Oxygen and water vapor absorption
 Temperature and humidity sounding
18th IHP Training Course

11/4/2008

Atmospheric Transmittance
InfraredUltra- Visible
VioletH2O
O３

CO2

Microwave

O３

H2O

O２
H2O

1/1000ｍｍ

CO2

Wavelength

H2O

1/10ｍｍhttp://earthobservatory.nasa.gov

11/4/2008

18th IHP Training Course

Meteosat infrared imagery

By courtesy of Prof. Raschke
18th IHP Training Course

11/4/2008

Microwave imagery: 6GHz
http://sharaku.eorc.jaxa.jp/AMSR/index_j.htm

18th IHP Training Course

11/4/2008

Microwave imagery: 36GHz
http://sharaku.eorc.jaxa.jp/AMSR/index_j.htm

18th IHP Training Course

11/4/2008

Microwave imagery: 89GHz
http://sharaku.eorc.jaxa.jp/AMSR/index_j.htm

18th IHP Training Course

11/4/2008

Microwave imagery: Retrieved rainfall
http://sharaku.eorc.jaxa.jp/AMSR/index_j.htm

18th IHP Training Course

11/4/2008

TRMM PR/TMI/VIRS

A variety of satellite sensors
 Visible/IR imager
Cloud and aerosol properties
Terra MODIS
trmm.gsfc.nasa.gov/publications_dir/
extreme_events.html

 Microwave radiometer

Cloud water & rain rate
 SST and near-surface wind Precipitable waterwww.eorc.jaxa.jp/imgdata/topics/
divide/atmosphere.html

 IR & microwave soundersT and qv profiles

Aqua AIRS RadarPrecipitation & cloud profiles

CloudSat CPR

www.jpl.nasa.gov/news/

www.cloudsat.cira.colostate.edu/index.php

18th IHP Training Course

11/4/2008

Expand more!
 More channels on a single sensor

MODIS (Terra & Aqua): visible/IR imager with 36 channels
 AIRS (Aqua): IR sounder with 2,378 (!) channels
 More sensors on a single satellite

5 sensors on TRMM, 5 on Terra, 6 on Aqua, and 12 on MetOp.
 NPOESS will have 4-9 sensors per satellite.

 More satellites collaborating together

A-Train: 6 satellites incl. CloudSat and Aqua fly in formation.
 GPM: consists of a core satellite and ~8 constellation satellites.
 More “active”

Radars onboard: TRMM, CloudSat, EarthCARE, and GPM.
 Lidars onboard: CALIPSO and EarthCARE
 More international
TRMM (US/JP), EarthCARE (JP/EU), and GPM (US/JP+)
18th IHP Training Course

11/4/2008

Lecture plan summary -1


L0: Introduction
Lecturer: Hirohiko Masunaga (Nagoya University)
 Guidance and outline of the IHP training course

L1: Overview of Meteorological Satellites in Orbit
Lecturer: Hirohiko Masunaga (Nagoya University)
 A brief summary of satellites and sensors being used for
meteorological research and weather forecasting.


L2: Satellite Remote Sensing of Clouds by Passive Sensors
Lecturer: Kazuaki Kawamoto (Nagasaki University)
 Methodologies to retrieve cloud physical properties from space Applications to climate studies

L3: Satellite Remote Sensing of Clouds by Active Sensors
Lecturer: Hajime Okamoto (Tohoku University)
 Cloud profiling by ground-based and spaceborne W-band radars Current and future satellite missions carrying cloud profilingradars

18th IHP Training Course

11/4/2008

Lecture plan summary -2


L4: Satellite Remote Sensing of Aerosols







L5: Satellite Remote Sensing by Temperature and Humidity Sounders







Lecturer: Kozo Okamoto (Japan Meteorological Agency)
Methodologies of atmospheric sounding from space
Application to data assimilation

L6: Geostationary Meteorological Satellites






Lecturer: Itaru Sano (Kinki University)
Methodologies to retrieve aerosol optical properties from space
Applications to climate and environmental studies

Lecturer: Atsushi Higuchi (Chiba University)
Introduction to existing geostationary satellites for meteorologicalapplications
Cloud monitoring by GMS/MTSAT : physical principles and applications

L7: Tropical Rainfall Measuring Mission (TRMM)

Lecturer: Kenji Nakamura (Nagoya University)
Mission outline and precipitation data products available
Applications to climate studies and extreme weather monitoring
18th IHP Training Course

11/4/2008

Lecture plan summary -3


L8: Space borne Precipitation Radars
Lecturer: Toshio Iguchi (Natl Institute of Info and Comm Tech) Rainfall profiling by space borne K-band radars Current and future satellite missions carrying precipitation radars

L9: Precipitation Retrieval by Microwave Radiometers
Lecturer: Kazumasa Aonashi (Meteorological Research Institute) Methodologies to retrieve precipitation from space Applications to data assimilation

L10: Global Satellite Mapping of Precipitation (GSMaP)
Lecturer: Tmoo Ushio (Osaka University) Introduction to the GSMaP project Retrieval algorithms and data products

L11: Greenhouse Gas Observing Satellite (GOSAT)
Lecturer: Gen Inoue (Research Institute for Humanity and Nature) Introductions to the GOSAT project Retrieval methodology and expected outcome.


18th IHP Training Course

11/4/2008

Practice and tech tour summary P1: Geostationary Meteorological Satellite (GMS) Data

Analysis

Lecturer: Munehisa K. Yamamoto (Chiba University) On-site computer exercise to analyze GMS data

 P2: Real-time Global Precipitation Data Analysis

Lecturer: Takuji Kubota (JAXA) On-site computer exercise to analyze TRMM and Global
Satellite Mapping of Precipitation (GSMaP) data

 Technical Tour to the Japan Aerospace Exploration

Agency (JAXA) in Tsukuba

Guided tour Briefing for satellite data/images access through JAXA EORC
WWW site lectured by Misako Kachi (JAXA)


18th IHP Training Course

11/4/2008

L2. Satellite Remote Sensing of Clouds by
Passive Sensors-Methodologies to retrieve cloud physical properties
from space-Applications to climate studies

Kazuaki Kawamoto
Faculty of Environmental Studies
Nagasaki University

What is Remote Sensing?
To measure the characteristics (quality, quantity, etc.)
of targets from remote place without touching
Electromagnetic wave is mostly used.

Inverse and forward problems ~Dragon and tracks~(Bohren and Huffman 1983)(Liou 2002)

Importance of clouds in the climate system
Radiation budget(reflection of solar radiation andabsorption/re-emission of earth’s radiation)
Low clouds: cooling effectHigh clouds: warming effectInteractions with aerosols
Contribution to the water cycle through precipitation

Importance of cloud microphysics
Optical depth (τc) Effective Particle Radius (re)・ influence on cloud radiative properties (reflectivity,transmissivity and emissivity)
・development of clouds and precipitation
・ interaction with aerosols (aerosol indirect effect)

Cf. Aerosol direct effect=> scattering and absorption of solar radiation by aerosolsCloud Physical Properties
Cloud amount (cloud fraction)
Cloud top height (cloud top pressure)Cloud optical depth (thickness)
Cloud geometrical thickness
Cloud thermodynamic phase (water or ice)liquid water content, ice water content
liquid water path, ice water path
Cloud droplet size (mode particle radius, effective particle radius)
Cloud reflectivity
Cloud transmissivity
Cloud emissivity

Basic principles to retrieve cloud properties
Passive method uses reflected solar radiation by clouds

Visible radiation(0.64micron) => little (negligible) water absorption=> cloud optical depth
Near-Infrared (2.16 or 3.7 micron) => moderate water absorption=> cloud effective particle
Infrared (11micron)=> emission from the cloud top=> cloud top temperature

Cloud Microphysics
cloud optical thickness∞ 2
τλ =πr Qext(λ,r)n(r)drdL
L 0

∫ ∫

the effective radius of cloud droplets
πr3n(r)drre ≡ ∫

2n(r)drπr∫

Cloud droplet size distribution：log-normal assumed0 )2 
N
(lnr
−
lnr

n(r) =
exp−

2σ 2
2πσ



Schematic of passive remote sensing of clouds

For near-infrared radiation (particle size-sensitive)
solar incidence
solar incidence
more reflection

less reflection

more absorption
less absorption

Small droplet

large droplet

For visible radiation (optical depth-sensitive)
solar incidence

solar incidence

more reflection
less reflection

optically thin cloudoptically thick cloudSimultaneous determination of τc and re
Reflected solar radiation by clouds
CH1. 0.64micron (no abs.), CH3. Near-Infra 3.73micron (moderate abs.)

Nakajima and Nakajima 1995

Forward calculation
τc, re ,Tc. angular conditions, water vapor and temperature profiles
Radiative Transfer Calculations

Simulated satellite signals

Look-Up tables

Then,

Sensor-measured signal
Comparison

Look-Up Table(pre-calculated solutions from radiative transfer)
Iterations will be done until the best solution is determined.

History of remote sensing of cloud microphysics
Pioneering measurements at NIR (planetary sciences)
Blau et al. (1966)
Hovis and Tobin (1967)

Aircraft measurements (NIR:1.6, 2.2μm)
Twomey and Cocks (1982)
Nakajima and King (1990)
Rawlins and Foot (1990)
Nakajima et al. (1991)

Satellite observations (NIR:3.7μm) on a regional scale
Platnick and Twomey (1994)
Nakajima and Nakajima (1995)
Platnick and Valero (1995)
Minnis et al. (1998)

Satellite observations (NIR:3.7μm) on a global scale
Han et al. (1994) using ISCCP data
Kawamoto et al. (2001)over ocean
over land1Frequency

0.8

0.6

0.4

0.2

0
4

6

8

10

12

14

16

Effective Particle Radius

18

20(micron)Climate Applications:
Relationships of clouds
with aerosols and precipitation

Datasets used here
Aerosol amount
Numerical simulation SO4, NH4, NO3, EC, OC by I. Uno (Kyushu U.)
inputting the emission inventory

Low cloud properties (Tc > 273K)(optical depth and effective particle size)
AVHRR remote sensing by Kawamoto et al. (2001)

Preceding studies on aerosol-cloud relationships over Chinamodel aerosol vs. ISCCP tau

SO2 emission and re
Kawamoto et al. 2004, Atmos. Res.

Cloud effective particle radius(micron)

Chameides et al. 2002, JGR

12

11

10

9

8

7
100

1000

SO2 emission

4

10(ton/year)

Map of τ in April, July and October in 1995

Apr.

July

0

10

20

Oct.

30

40

Map of re in April, July and October in 1995

Apr.

July
5

8

10

12

Oct.
15

18

20

(µm)

Low cloud optical depth

20

15

10

5

0
8

9 10

20

30

40

50

60

2

70 (mg/m )Anthropogenic aerosol amount

Relationship between Ma and τ in July in 1995

Low cloud effective particle radius(µm ) 14

13

12

11

10

9

8

7

6
8

9 10

20

30

40

50

60

2

70 (mg/m )Anthropogenic aerosol amount

Relationship between Ma and re in July in 1995

2

Integrated cloud droplet number(1/cm )

6

10

5

10

8

9 10

20

30

40

50

60

2

70 (mg/m )Anthropogenic aerosol amount

Relationship between Ma and Nc in July in 1995

Datasets used here
Precipitation amounta merged algorithm based on Xie and Arkin (1997)
using gauges and satellites, 0.5-degree coverage
Low cloud properties (Tc > 273K)
Optical depth and effective particle size
AVHRR remote sensing by Kawamoto et al. (2001)
0.5-degree coverage(mm/month)

Amazon basin (W50-W70, EQ-S15) monthly-mean

140

precipitation amountPrecipitation amount

120

100

80

60

40

20

0

J

F

M

A

M

J

J

Month

A

S

O

N

D

(%) 45
low cloud
middle cloud
high cloud

40

cloud amounts

35

30

25

20

15

10

5

J

F

M

A

M

J

J

Month

A

S

O

N

D

Precipitation amount(mm/month)120

precipitation amount

J
F
M
A
M
J
J

Month

low re

100
9.5

80
9

60

40
8.5

20
8

A
S
O
N
D

Low cloud effective particle radius (micron)10(mm/month)10

precipitation amount120

low tau

100
8

80

7

60

6

5
40

4
20
3

J

F

M

A

M

J

J

Month

A

S

O

N

D

Low cloud optical depth

Precipitation amount9

(mm/month)40

precipitation amount120

tot tau

100
30
80
25
60
20
40
15
20
10

J

F

M

A

M

J

J

Month

A

S

O

N

D

Total cloud optical depth

Precipitation amount35

(micron)Low cloud effective particle radius

16

14

12

10

8

6

4
0

20

40

60

80

Precipitation amount

100

120

(mm/month)(1/cm2)

Columnar low cloud droplet number

8

10

7

10

6

10

5

10

20

40

60

80

100

120

(mm/month)

Precipitation amount

Rainy period

more precipitation
less biomass burningdry period

less precipitation
active biomass burningSummary
Retrieval principles using passive remote sensing of cloud properties
Visible => negligible absorption, NIR => moderate absorption

Comparisons between aerosol amount and low-level water cloudproperties over the East Asia are performed.
Results support the Twomey effect.
(If more aerosols, cloud optical depth increases.
effective particle radius decreases
through aerosol - cloud interactions.)Cloud-precipitation relationships are examined.
Low clouds generate little rain generally.
If precipitation (CCN scavenging) , thenlow cloud droplet size
low cloud optical depth
Precipitation scavenging would be an important factor for
determining cloud properties
Low cloud properties are controlled by aerosols and precipitation.

Lecture in the 18th IHP Training Course in
Nagoya Univ., Nov.5, 2008

Satellite remote sensing of clouds
by active sensors

Hajime Okamoto(Tohoku University)

Contents
1.What is active remote sensing
2. About Scattering and absorption by particles
3. Lidar and radar equations
4. Why clouds are important.
5. Observations of clouds from radar and lidar
6. Evaluation of the Clouds from the Atmospheric
General Circulation Model
7. Retrieval of ice microphysics
by synergy use of CloudSat and CALIPSO data
8. Future satellite mission with radar1. What is active remote sensing

Passive sensing relies on measuring natural levels ofradiation, e.g., the radiation emitted from the sunlightreflected by the clouds.
Active sensing makes use of electromagnetic energy
transmitted from a specially chosen source to sometarget (clouds) and then monitoring the interactions
between the target and the radiation.
(Stephens 1983)

2. About Scattering and absorption by particles

Scattering problem by spherical particle has been
analytically solved from Maxwell equations byLorentz and Mie in early 1900, (Mie theory).
According to the recent development of computerresources the Mie theory has been widely used.
Scattering properties depend on
size, wavelength, refractive index (real and imaginarypart).

Deﬁnition of Extinction and Poynting vector

E 2 ,H 2 :Electric and magnetic
ﬁelds outside particle

E2 = Ei + Es

H2 = Hi + Hs
E i = E o exp(ik " x # i$t )H i = H oexp(ik " x # iwt)!

S

:Poynting outside the particle

S=

1
Re{E 2 " H 2 }2

1
Re{(E i + E s ) " (H i + H s )#
2
#
#
1
1
= Re{E i " H i } + Re{E s " H s }
2
2
#
#
1
+ Re{E i " H s + E s " H i }2#1
Si = Re{E i " H i },2#
1
Ss = Re{E s " H s },2##
1
Sext = Re{E i " H s + E s " H i }.
2
S = Si + Ss + Sext=

Particle embedded in imaginary spherea
W = " # S • e dA
ar

A

Wa > 0;Wa = Wi "Ws + Wext
Wa = " # (Si + Ss + Sext ) • e r dA
A

Wi = " # Si • e r dA
A

(Bohren and Huffman 1983)

Ws = # Ss • e r dA
A

Wext = " # Sext • e r dA
A

$Wa = Wi "Ws + Wext
Wi = 0
Wext = Wa + Ws

Extinction = absorption+scatteringCext =

Wext
Ii

Extinction cross section

Cabs =

Wabs
Ii

Absorption cross section

Csca =

Wsca
Ii

Scattering cross section" Cext = Cabs + Csca!

Extinction cross section=Scattering cross section+Absorption cross sectionElectromagnetic properties of particles depend on
1) Shape2) Size distribution3) Chemical composition/phase ; refractive index(1) (wavelength dependence)4) Macrophysics ; thickness, cloud altitude(From Murakami )From Murakami

For small particles compared with the wavelength,
Rayleigh scattering theory is applicable.
For large particles compared with the wavelength,
geometrical optics can be used.
Approximations of scattering theories forarbitrary shaped particles have been also
developed, e.g., discrete dipole approximation.

Treatment of non-sphericity
We need to define radius for non-spherical particle.
We introduce mass equivalent radius$ 3m '1/ 3
req = &)
% 4"# (

3
4
"req # = m
3

!
!

!

req

r

! the same
! particles have
When the two
radius , it means both have the same mass.

reqUsing a scattering theory, e.g., Mie theory,the following physical quantities can be obtained.
Backscattering cross section [!m^2/ster]Cbk , p (r )Extinction cross section [!m^2]

Cext, p (r )!
Size distribution function [1/!m/m^3]dn(r )dr!

1 dn(r )Cbk , p (r )dr$4#
dr

Lidar backscattering coefficient[1/m/ster]!

" true, p =Extinction coefficient [1/m]:" ext, p = #!

dn(r )
Cext, p (r )drdrdn(r ) 3

Effective radius [!m] :Reff =!

!

" dr r drdn(r ) 2" dr r dr

3. Lidar and radar equations

Lidar equation
Lidar uses laser beam where the wavelength is in visible orinfrared regions.
Relation between received power Pr and backscattering
coefficient is described by Lidar equation.
!RPr, li ( R) =CL Pin, li"R
# obs ( R)R2

"obs (R) = ("true ,P (R)+ "true ,Ray (R))# exp($2% (R))
!
R

!

!

" = % (# ext ,P (R $)+ # ext ,Ray (R $))dR $0

R: distance between the particle
and the lidar

Radar equation :Radar equation is similar to lidar equation.
Radar wavelength is in microwave region,
e.g., mm- to cm.
Pr (R) =
Ze =

!
!

C "R
R2

& R
)
K Ze (R)exp(#2 % $ ext ,i (Ri )dRi+
' 0
*
2

"4

dn(r)
C bk (r)dr
2 $
5
dr
# K

(m2 " 1)K =(m2 + 2)dBZe = 10log10 Ze!

Radar reflectivity factor: Ze

Radar signal;

Ze " r 6

Lidar signal ; " # rfor one particle

2

!

!

!

!

-3

95GHz radar (ze=-40dBZe)
Lidar (beta=10^-6[1/m/ster])

IWC [g/m^3]-4

Radar is sensitive to large
particles.
Lidar is sensitive to small
particles.

-5

10

-6

10

-7

10

9

10

2

3

4

5 6 7 89

2

100
Reff [um]34

5 6 789

1000

Minimum IWC as a function ofeffective radius#1

Differences between lidar
and radar.

10

10

3

for a IWC
" ~ reff-2

10

Ze ~ reff2

Attenuation (extinction) should be taken into account forinterpretation. attenuation due to clouds, precipitation, gasses

dBZeatte = dBZetrue " dBcl " dBgas " dBredome " dB precip!

"atte (R) = ("aero (R)+ "cl (R)+ "gass (R))exp(#2$ (R))
R!

" (R) = % (# aero (R)+ $ (R)# cl (R)+ # gass (R))dR
0!

Attenuation due to gas in tropics
3

20x10

Height [m]

15

10

5

0

1

2

3

4

5

Attenuation [dB] due to gaseous absorption

6

4. Why clouds are important.

Issues and motivation
Cloud microphysics is a key component; radiation and these leads to thechange in atmospheric dynamics and this generates cloud and aerosols (cloudaerosol feedback loops).
Difficulties in the studies of cloud feedback exist. Passive instruments oncurrent satellites provide integrate quantities since they measure radiation fromcolumn. Large uncertainties exist in the distribution of clouds and aerosols,e.g., cloud vertical structure and microphysics, i.e., phase, size, ice/liquid watercontent and types and scattering characteristics of aerosols.

Towards the evaluation of the role of clouds and aerosols on the climate system.
Cloud feedback process

distribution of cloud, aerosol, precipitation

circulation of atmosphere andwater
convection; horizontal andvertical transport

Energy distribution(radiation and latentheat)

To validate the numerical models, we also need knowledge of cloud
distribution and cloud microphysics.
Another way is the direct comparisons of signals where physically-based
simulation of signals of satellite sensors can be used from the output of
numerical models (in order to take into account observation conditions).
For the purposes, we need sophisticated treatment in the modeling, e.g.,
scattering models of clouds and aerosol particles from visible to mmwavelength.

5. Observations of clouds from radar and lidar

data sources(I) Ship-borne cloud radar/lidar
experiments using R/V Mirai since 2001;
The radar has the same frequency as CloudSAT
And the lidar is the same as CALIPSO.
(II) CloudSAT and CALIPSO
were successfully launched in April 2006.
CloudSAT : 95GHz cloud profiling radar
CALIPSO : 532nm/1064nm lidar

Cruise tracks of R/V Mirai

5-1. Analysis of radar/lidar data during R/V Mirai cruise
5-1-1 mid-latitude case

Mirai cruise track in MR01-K02 from May 1427, 2001

lidar

15

16

17

18

19

20
21
22
Date in May, 2001

23

24

25

26

27

Radar
-60

-50

-40

-30 -20
dBZe

-10

0

10

10000

Height [m]Height [m]

11000
10000
9000
8000
7000
6000
5000
4000
3000
2000
1000

8000
6000
4000
2000

14

15

16

17

18

19

-7.0

20
21
22
Date in May, 2001

-6.0
-5.0
-4.0
log Beta (532nm) [1/m/ster]

23

24

25

26

Lidar

27

28

Cloud vertical structures obtained from radar/lidar
in Western Pacific Ocean near Japan in 2001
12x10

3

10

Height [m]8Triple peaks in cloud fractions.

6
C1
C2
C3
C4
C5

4

2

0

10

20

30

40

50

60

Mean cloud fraction for whole records [%]Cloud and hydrometeor fractionin May 2001 (Okamoto et al., JGR 2007)
C1 : radar only :Radar signal is contaminated by drizzle and precip.
C5 : C1 +lidar cloud bottom

5-1-2 Tropical Western Pacific Ocean casein Sep.21-Dec. 17, 2001

Cloud vertical structures obtained from radar/lidar
in Tropical Western Pacific Ocean in 2001

3

16x10

14
C1
C5

Height [m]12More homogeneous structure in verticaldirection compared with mid-latitude.

10
8

Maximum of cloud fraction
is 20% (smaller ) and is located at 12km.

6
4
2
0

5

10

15

20

25

30

Cloud fraction [%]Tropical Western Pacific Ocean
near Japan, 2001)

Precipitation frequency
3

16x10

C1
C5
0dBZe <
-10dBZe <-30dBZe <14

Height [%]

12
10
8
6
4
2
0

5

10

15

20

Cloud fraction [%]25

30

Number of cloud layers
50

Fraction [%]

40
30
20
10
0

0

1

2
Number of layers

34

6. Evaluation of the Clouds from the Atmospheric
General Circulation Model: Simulation of cloud radar and lidar signals from model
outputs and validations of the GCM.

1. Model: CCSR-NIES-FRCGC GCM, T106(~100km) L20
NCEP/NCAR reanalysis data is used to nudge the GCM for each 6hours. Four aerosols are treated in SPRINTARS (Takemura et al., JGR
2005) to simulate lidar signals.
To compare, sampling of the clouds for the grid along the cruise track is
performed. Time resolution of output is 6 hours/40 minutes forcomparison with observations.
2. Parameters used for the comparisons;Large scale condensation (CFls: cloud fraction, grid mean liquid watercontent LWCls/ice water content IWCls (3d))
Cumulus convection parameterization (Cfcu: cloud fraction (2d),
LWCcu, IWCcu
CFtotal=1-(1- CFls)(1- Cfcu)
Radar reflectivity factor dBZe
Lidar backscattering coefficientSimulation of CPR and lidar signals
Simulator of CPR and lidar signals (Okamoto et al., 2007,2008) for validation ofnumerical models.
CPR:
model output from GCM or JMA-NHM : cloud microphysics, precipitation (2D or
3D), cloud fraction. Attenuation of gas is included. (~5dB in tropics).
Lidar signals:
cloud part: consistent to CPR.
model out from SPTRINTARS (Takemura et al., 2005):
Four types of aerosols, sulfate, carbonaceous, sea-salt and dust are treated. Eachextinction is produced and converted to backscattering coefficient.
Use look up tables. (see also Nishizawa et al., 2007, 2008)***CloudSAT/CALIPSO simulators are also developed.

Comparison of observed and simulated cloud fraction
Height [m]

20x10

3

15
10
5
0
20

0

Height [m]

20x10

40
Date from Sep.21, 2001
20

60

80

40
60
80
Cloud fraction (C5) [%] for Obs.

Observation

100

3

15
10
5
0
20

0

20

40
Date from Sep.21, 2001
40
60
Cloud fraction [%] for R40

80

60

80

100

GCM

Height [m]

3

20x10

Example in Tropics, comparison of observed andsimulated Ze in CCSR-NIES-FRCGC GCM

15
10
5
0

20
Observed Ze-60

40
Date from Sep.21, 2001
-40

60

-20
dBZe for Obs.(C5)040
Date from Sep.21, 2001

60

80

3

20x10

15
10
5
0

Simulated Ze

20

-60

-40

-20
dBZe for SP(R40)80

0Simulated Ze has narrower distribution in highclouds and smaller signals.

Example in Tropics, comparison of observed andsimulated beta in due to clouds
3

Height [m]

20x10

15
10
5
0

Observed beta 20

40
Date from Sep.21, 2001

60

-7

-6
-5
-4
Log Beta [1/m/ster] at 1064nm for obs.(C5)20

40date from Sep.21, 2001

-7

-6
-5
-4
Log Beta [1/m//ster] at 1064nm for SP(R40)

80

-3

3

Height [m]

20x10

15
10
5
0

Simulated beta

60

80

-3

Simulated beta has larger beta in midlevel clouds.

Treatment of sub-grid scale clouds in the
model

3

Height [m]

20x10

15
10
5
0

Height [m]

20x10

20

40
Date from Sep.21, 2001

60

-7

-6
-5
-4
Log Beta [1/m/ster] at 1064nm for obs.(C5)

80

-3

3

15
10
5
0
0

20

40
Date from Sep.21 2001

60

-7
-6
-5
-4
-3
Log Beta [1/m/ster] at 1064nm for GCM (subgrid-R40)

80

9#',5"):#*(#4(1%#23(4"516)#*$&6.&&*(#$:&"056)#*(5*3(C9D()*(E"#,)15%(F&:6&"*(!51)4)1(G1&5*
H*(=II78!"#$%&'()*+,,&"-%#./%&0&%(1%#23(4"516)#*78(92'2%2:(1#*0&16)#*,5"5'&6&");56)#*<=8(>)4&(1?1%&@(3)::),56)#*,"#1&::(A6&"')*5%(0&%#1)6?
5*3(5)"('#6)#*B<
3

12x10

10

Height [m]86Obs.(C5)
SP(R40)SP(HJI2001)
SP(original)

42E"#,)1:

0

10

20

30

40

50

60

Mean cloud fraction [%]H*(')3/%56)623&
Okamoto et al., 2008, JGR)

Okamoto et al., 2007, JGR)

7. Retrieval of ice microphysics
by synergy use of CloudSat and CALIPSO data

Acknowledge: CloudSat science team and CALIPSO team

1. Motivation
Study of global distribution of ice microphysics from CloudSat and
CALIPSO Synergy.

2. Problem.
There are two issues in the analyses.
1) Vertical Feature mask (VFM) has some problem for thedetection of clouds.
2) Specular reflection in CALIPSO is often observed (Winker ).
This turns out to be obstacle for the radar-lidar retrievals, i.e.,
not possible to find results or may contain large errors.

3. Efforts.
We develop cloud mask scheme for CALIPSO.
The radar-lidar algorithm (Okamoto et al., 2003 JGR) is modified inorder to treat specular reflection.

NASA Langely’s Vertical Feature mask (VFM)

Cloud mask for CALIPSO ( TU mask)(1) Signal>threshold value(2) More than 13 pixels satisfy the criterion (1)among surrounding 5*5 (=25) pixels.

Zonal mean cloud frequency of occurrence from CloudSat and
CALIPSO in September to November 2006

CloudSatCALIPSO (TU)

CloudSat&CALIPSO

CloudSat or CALIPSO(TU)(Hagihara et al., 2008)

1. Characteristic of the radar-lidar algorithm
modified from Okamoto et al., 2003 JGR.
(1) Input observables are Ze, ! and depolarization ratio.
(2) Forward type algorithm.
Extinction to backscattering ratio is not assumed to be constant as in Klett
type one.
New feature is that the ratio of horizontally oriented crystals to the total
number is also retrieved. In addition to the former look up tables, new
LUTs introduced for oriented crystals, i.e., two radar (Ze, extinction for
10000 reff bins) and two lidar (beta and extinction for 10000 reff bins).
The algorithm retrieves effective radius and IWC that match observables.
Ze and extinction for oriented crystals are calculated by the discrete dipole
approximation (Sato and Okamoto 2006 JGR). ! and extinction are
estimated by the modified Kirchhoff approximation (Iwasaki and Okamoto
2001, Appl. Opt.)(3) Attenuation correction is carried out by the retrieved microphysics.

Observed Ze and !

Ze!

Retrieved effective radius and IWC from our conventional algorithm

effective radius

IWC

Large backscattering and low depolarization ratio
denote specular reflection.
We found difficulties in the retrieval of microphysics
In these regions when our conventional algorithm isapplied .

How to treat specular reflection in lidar signals.

Sassen and Benson (2001, JAS)

Sassen and Benson (2001, JAS)estimated the ratio X of number
concentration of randomly
oriented crystals and that ofhorizontally oriented crystals fromdepolarization ratio, assuming therandomly oriented crystals havedep.=0.4 and that for horizontallyoriented crystal =0. ! (2d) /! (3d)=360.
(They stated 360 might be lower
than actual).

Algorithm flow

The ratio ! (2d) /! (3d) depends on size. Thus for the given depolarization ratio,
N(2d)/Nt depends on size.

Results from the new radar-lidar algorithm

effective radius from new algorithm

effective radius from conventional algorithm

IWC from new algorithm

Zonal mean of ice microphysics fromCloudSat/CALIPSO in Oct.2006

Effective radius

Cloud fraction

IWC

N(2d)/NtSummary
0. VFM seems to overestimate cloud fraction and new cloudmask for CALIPSO is developed.
1. We develop new radar-lidar retrieval algorithm that cantreat the mixture of oriented crystals and random ones.
2. We use the modified Kirhchoff approximation for lidar
specular reflection and discrete dipole approximation for Ze
for scattering signatures of oriented crystals.
3. Unless look up tables of oriented crystals are implemented,
it is not possible to analyze specular region.
4. Zonal mean of ice cloud microphysics is derived. There aresmall particles (~<50µm) in the upper layers. The size inspecular region is large ~200µm.
5. Ze-IWC relation applied to CloudSat is carried out andcompared with this study.

7. Future satellite mission with radarBeyond CloudSAT/CALIPSO
EarthCARE in 2013
(ESA-JAXA mission)• Cloud radar with Doppler function,• High spectral resolution lidar• Broad Band Radiometer• Multi spectral Imager
We develop algorithms for clouds andaerosols for EarthCARE with JAXA.

Summary1) We performed shipbased radar and lidar observations in Tropics, midlatitude and poler regions from 2001 to present.
2) We develop the radar an lidar signal simulator for shipbased radar/lidar andfor CloudSAT and CALIPSO from the model outputs. We tested the cloudfields from the ship- and satellite-borne with those simulated fromSPRINTARS (GCM).
Findings are;General pattern of clouds and aerosols is reasonably reproduced in mid-latitude
and less agreements in the tropics. Overestimation of high level clouds insimulation than in observations , due to the over-prediction of frequency of
deep convection.
The effective radius is too small in mid- and high level clouds.
The simulated low clouds tend to be too thick.
The Mirai data are distributed via ftp server from us.

2) Several retrieval algorithms have been developed for microphysics ofclouds and aerosols from synergy use of radar/lidar data. Our radar/lidar
and radar-Doppler algorithms show the smallest errors (bias/dispersion)
among teams through blind tests (Heymsfield et al., JAMC 2007 inpress).
3) Correction of specular reflection is implemented and seems to
improve the applicability of the radar/lidar method.
4) Validation of the algorithms of CloudSAT/CALIPSO against the insitu measurements are tried. Some agreements and some underestimation.

References
Heymsfield, A. J., A. Protat, R. Austin, D. Bouniol, R. Hogan, J. Delaoe, H. Okamoto, K. Sato, G. Zadelhoff, D. Donovan, Z.
Wang, Testing and Evaluation of Ice Water Content Retrieval Methods using Radar and Ancillary Measurements, J. Appl.
Meteor.Clim, 2007 (in press)
Nishizawa, T., H. Okamoto, N. Sugimoto , I. Matsui, A. Shimizu , K. Aoki, An algorithm that retrieves aerosol properties fromdual-wavelength polarized lidar measurements, J. Geophys.Res., 112, D06212, doi:10.1029/2006JD007435, 2007
Nishizawa, T., H. Okamoto, N. Sugimoto , I. Matsui, A. Shimizu, Aerosol retrieval from dual-wavelength polarization lidar
measurements taken duringthe MR01K02 cruise of the RV Mirai and validation of a global aerosol transport model (J. Geophys.
Res., (2008 in press)
Okamoto, H., Information content of the 95GHz cloud radar signals: theoretical assessment of effect of non-sphericity by teh
discrete dipole approximation, Geophys. Res., 107(D22), 4628,
doi:10.1029/2001JD001386.
Okamoto, H., S. Iwasaki, M. Yasui, H. Horie, H. Kuroiwa, and H. Kumagai, An algorithm for retrieval of cloud microphysics using
95-GHz cloud radar and lidar. J. Geophys. Res., 108(D7), 4226, doi:10.1029/2001JD001225, 2003
Okamoto, H., T. Nishizawa, T. Takemura, H. Kumagai, H. Kuroiwa, N. Sugimoto, I. Matsui, A. Shimizu, A. Kamei, S. Emori, andT. Nakajima, Vertical cloud structure observed from shipborne radar and lidar, Part (I) : mid-latitude case study during theMR01/K02 cruise of the R/V Mirai, J. Geophys. Res, 112, D08216, doi:10.1029/2006JD007628, 2007
Okamoto, H., T. Nishizawa, T. Takemura, K. Sato, H. Kumagai, Y. Ohno, N. Sugimoto, I. Matsui, A. Shimizu, and T. Nakajima,
Vertical cloud properties in Tropical Western Pacific Ocean: Validation of CCSR/NIES/FRCGC GCM by ship-borne radar andlidar, J. Geophys. Res., (2008 in press)
Sato, K., and H. Okamoto (2006), Characterization of Ze and LDR of nonspherical and inhomogeneous ice particles for 95-GHz
cloud radar: Its implication to microphysical retrievals, J. Geophys. Res., 111, D22213, doi:10.1029/2005JD006959.
Sato, K., Okamoto, M. Yamamoto,H. S. Fukao, Kumagai, Y. Ohno,H. Horie,and M.Abo 95-GHz Doppler radar and lidar synergy
for simultaneous ice microphysics and in-cloud vertical air motion retrieval. J. Geophys. Res., (2008, in press)

Backup!"#$"%!&'()'*%+#,"-.%/#01%2+3456%7''(%30%8$9%:;6%7'';
3.-3%+$-.-<%=>0,?@$3A=BCDE@2%"9%2+34)F0G4%7''(=>0,?@BHA=BCDE@2%0G.#I$--.+>0-.%30%!"#$"%+#,"-.%3#$+J4

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2+34756%S<7'%QH=%M'0F6%;T0RN
2+34%756%7'<:*QH=%M'0F6%;T0RN
2+34%7T6%S<:*%M'0F6%S'4*0RN
F0G4%*6%S<7'%M'0F6%S'4*0RN

U

F0G47:6%S<7'%M'0F6%S'4*0RN
F0G4%7*6%7'<:*%M'0F6%S'4*0RN

A,/,#$3(<$..,#/("+#,$+)/$*3>(+"123$.,'@?,("+(3$#4,#($*)-,
BCD1($/'(+1$33,#($.(E%FD1@(:*)-,(BCD1G+,/+"."-".>("+(/).
+255"=",/.H
I/=,#.$"/.",+J;)1<$#"+)/()5(#$'$#(#,53,=."-".>(5$=.)#(?,(*>!"#$"%#$'$#()*+,#-$.")/($/'(+"123$.,'()/,
5#)1(06&789:&0@!"#$"%&$'$#()*+,#-$.")/
3

Height [m]

15x10

Obs.
SP
10

5

-60

0"123$.,'(+"4/$3(5)#(!"#$"%#$'$#*$+,'()/(06&789:&0

-40

-20
Mean dBZe,gm!,$/(?,

020

Differences between space-borne and ground or shipborne systems: based on AGCM-SPRINTARSCALIPSO case (similar to ATLID)

Ground-base lidar caseOverpass on Oct.24, 20:20 UTC
3

14x10

Oct.24, 2020, 2006
Mirai radar
CloudSAT

Height [m]1210Height [m]

15x10

8

Mirai lidar
CALIPSO

3

10

6

5

4
2
0
-60

-40

-20

0

dBZe

Comparisons of Mirai radarand CloudSAT

-7.0

-6.5

-6.0
-5.5
-5.0
-4.5
log Beta [1/m/ster] at 1024nm-4.0Comparisons of Mirai radarand CloudSAT

Aerosol
properties in
MR01-K02
Water soluble

Sea-salt

dust(Nishizawa et al., 2007,
JGR)

Validation of CloudSAT/CALIPSO algorithms(collaborate with J. Mace)!"#$%"&'()$"*+(#*),$-.)/(-+01234/256718)%*9-.:'$"0);%#<)/-=0"=*"0)>'#"$)/-=#"=#),$-.
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3

Height [m]

15x10

10
5

11

12

13

14

15

16

Latitude

CloudSAT

-40

-30

-20

-10
dBZe

0

10

3

Height [m]

15x10

10
5

11

CALIPSO

12

13

14

15

16

Latitude
-7

-6

-5
-4
log Beta [1/m/ster] at 1064nm-3

3

Height [m]

15x10

10
5

11

12

13

14

15

16

Latitude

Effective radius
0

50

100
Effective radius [!m]

150

200

3

Height [m]

15x10

10
5

11

IWC

12

13

14

15

16

Latitude-4-3-2
Log IWC [g/m**3]-1

0

CVI vs radar/lidar algorithm, Ze-T algorithm
Log IWC [g/m**3]

-1.0

CVI
CloudSAT-CALIPSO

-1.5
-2.0
-2.5
-3.0
-3.5
-4.0
11

12

!(#$

13
14
Latitude on July 22, 2007

15

16

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Aerosol Remote Sensing
Itaru Sano (Kinki University)contents
1.
2.

3.

4.

Introduction
Satellite aerosol remote sensing
2.1 Basic concept
2.2 Light scattering by aerosols
2.3 Satellite aerosol retrieval
Validation of aerosol remote sensing
3.1 Ground-based sun photometry
3.2 Model simulation
Future satellite sensors

Contents

1.
2.

3.

4.

Introduction
Satellite aerosol remote sensing
2.1 Basic concept
2.2 Light scattering by aerosols
2.3 Satellite aerosol retrieval
Validation of aerosol remote sensing
3.1 Ground-based sun photometry
3.2 Model simulation
Future satellite sensors

Role of aerosols shown in 4th-IPCC report

Aerosols

IPCC, 2007: Summary for Policymakers.
In: Climate Change 2007: The Physical Science Basis.

Radiation forcing of aerosols
solar radiation

aerosol

cloud

Direct effects Indirect effects

Radiation budget
circulation fieldcourtesy of Dr. M.Mukai

Role of aerosol study
Long standing climate need
# Negative and/or positive forcing of magnitudein the radiation budget# Exact magnitude with sign is currently still poor

Variable component of the atmosphere# Aerosols are highly variable and showscomplicated phenomena.
Aerosols should be precisely retrieved# for radiative transfer models ( Atmos. Corr. )# for GCM models# for PM2.5, PM10 (Air quality, health care etc.)

Contents

1.
2.

3.

4.

Introduction
Satellite aerosol remote sensing
2.1 Basic concept
2.2 Light scattering by aerosols
2.3 Satellite aerosol retrieval
Validation of aerosol remote sensing
3.1 Ground-based sun photometry
3.2 Model simulation
Future satellite sensors

Color of sky

Sun light scattering
by atmospheric ptls.

atmospheric particles
gas molecules

aerosols

solar radiation

sunset glow

long wavelength
short wavelengthｖ

blue skyWavelength (usually symbolized by λ)wavelength is the
distance between 2
consecutive peaks ina wavelong wavelength ;short wavelength ;http://www.lbl.gov/MicroWorlds/ALSTool/EMSpec/Spectrum of electromagnetic radiation
The spectrum is the distribution ofradiation according to the wavelength

http://www.lbl.gov/MicroWorlds/ALSTool/EMSpec/EMSpec2.html

Observing wavelength of POLDER sensor
equipped on the satellite ADEOS.443 .490 .565

.670

Pol.

Pol.

.765
.763

.865.910

[μm]

ADEOS
POLDER

.400

.500

.600

.700

Pol.

.800

.900

[μm]

Light scattering by ptls.

ｒ ： radius of ptl.
x = 2πr/λ
λ：wavelength; size parameter

Light scattering is most effective at x≈1
small ptl.
large ptl.

≈ short wavelength light≈ long"gas molecules provide blue sky
aerosols"
sunset glow

Classification of light scattering
by atmospheric ptls.
(µm)
103
102

ｒ 10

solar radiation

104

Geometric
Optics~rain drop
~haze ptl
~cloud ptl

1
10-1

Mie Sca~aerosol

Rayleigh Sca~gas mol.

10-2
10-3

1

10

102

103

104

wavelength (μm)

105

Aerosols (X≈1 )accord to the Mie scattering
Gas molecules (X << 1)"Rayleigh scattering

The spectrum information by satellite
provides characteristics of atmospheric ptl.

Contents

1.
2.

3.

4.

Introduction
Satellite aerosol remote sensing
2.1 Basic concept
2.2 Light scattering by aerosols
2.3 Satellite aerosol retrieval
Validation of aerosol remote sensing
3.1 Ground-based sun photometry
3.2 Model simulation
Future satellite sensors

2.2.1 Single scatteringsingle scattering pattern (phase function)(usually symbolized by P)

P is the angular distribution ;∫P (θ ) dθ / 4π = 1

of scattered light.

aerosol ptl

incident lightθ : scattering angle

scattered light- scattering plane -sensorX << 1 : Rayleigh scattering
Ref.S.Chandrasekhar(1960)

3
2
P (cos θ ) = (1 + cos θ )4• In the Rayleigh scattering approximation,a molecule (or a small particle) is considered as
an individual dipole.
• Because the sizes of atmospheric molecules aremuch smaller than the wavelengths of solar and
IR radiation, the scattering by atmospheric gasesis the Rayleigh scattering.

Rayleigh single scattering cross section : σ128π 5 2σ=α43λwhere α represents the polarizability⎛ m2 −1 ⎞
⎜⎜ 2
⎟⎟
α=
4π N ⎝ m + 2 ⎠3

N: number of mols
m : ref idx

The Rayleigh scattering cross section andhence optical depth are inversely proportional
to the fourth power of the wavelength.

blue color of the sky

Iλ ~

1

λ4

X≈１: Mie scattering
1.
2.

ref. Lorentz-Mie (~ 1900)

Ptl is a sphere,
Ptl is homogeneous(therefore it is characterized by asingle refractive index m=n - ik
at a given wavelength)
Mie theory requires refractive index of
ptl / medium.
m ≈ 1 for air
refractive index of ptl(i.e. composition of ptl )- on Mie scattering calculation Size parameter; X (r, λ) and refractive index m (n, k)
with Mie theory gives :Efficiency factors,
Qext, Qsca, Qabs
Amplitude function
phase matrix (phase function)

- on Mie scattering calculation ２ forward scattering is dominant in usual.

Shape of scattering ptl

sphere…………..

Lorentz-Mie (~ 1900)

Mie scattering
infinite cylindercoated sphere/core mantle
cluster of spheres………...Asano & Yamamoto(1973)
spheroidcluster of spheroid
Non-spherical
DDA………………………... Purcell & Pennypacker(1973)
Drain & Flatau
T-matrix……………………..M.Mishchenko(1990)

Hex. clm…………………… A.Macke(1993)

Heterogeneous sphere

Maxwell-Garnett
mixing rule

Bruggemanmixing ruleWSOC

m = n - ik

Parameters of aerosols

size

composition

shapeamountspheresize
distribution

Angstrom Refractive
Index
Index
αα

m = n - ki

OpticalThicknessτ τComposition of scattering ptl.
(complex refractive index)
m = n – ik
n : refractive index (0.55 µm)
1.38 : oceanic
1.43 : sulfate
1.53 : mineral dust
1.75 : sootk : index for absorption
3.7 E-9 : oceanic
1.0 E-8 : sulfate
5.5 E-3 : mineral dust
4.4 E-1 : sootref. Shettle & Fenn (1979)
d'Almeida(1991)

Size distribution of scattering ptl
Mode of aerosol particles
nucleation mode

(0.001 to 0.1 µm )gas to particle condensation

accumulation mode (0.1 to 1 µm)
coagulation and heterogeneous condensationcoarse mode

( > 1 µm)

origin in mechanical process, dust , sea-salt
1.0e+31.0e+2log-normal distribution

sum of log-normal dist.
n( r ) =ndN (r )dN (r )
=∑ i
dr
dr
i

Size DistributiondN (r )N
⎧ (ln r − ln rm ) ⎫=n( r ) =exp⎨−⎬
dr
2 ln 2 σ ⎭2π r ln σ⎩

1.0e+11.0e+01.0e-11.0e-2

rm1
σ1

rm2
σ2

1.0e-31.0e-41.0e-51.0e-6
1.0e-31.0e-21.0e-11.0e+0Radius (μm)

1.0e+1Size dist.
Number size distributionn( r ) =dN (r )
drVolume size distributiondV (r ) 4 3= π r n(r ) for total amount of particulate matterdr3

Surface size distributiondS (r )= 4π r 2 n(r )drfor heterogeneous chemistry2.2.2 Multiple scatteringLight scattering in the atmosphere
The solar radiation field (denoted by I ) is
traditionally considered as a sum of two distinctly
different components:
direct and diffuse : I = Idir + Idif
The direct radiation Idir is expressed byBeer-Bouguer-Lambert law, which states that theextinction process is linear in the intensity ofradiation and amount of matter.
Idir = I0 exp( - τ*)τ* : optical thickness of atmosphereτ* :optical thickness ← transmittanceτ*(λ) = ∫κext(λ) dz
clear

τ* depends on the wavelength⇒ Angstrom exponent (N.B.)

opaque

τ=0

z=z*

τ= τ∗

z=0

τ*

increasing（height

N.B.

τ* (λ) by Mie cal.

1.0

1.0
OC

OC

f aero0.8

0.6

size of aerosol～ 1 / α

0.6
faero

Optical Thickness

0.8

τ total
0.4

0.4

0.2
0.0
1.0

1.0e+3

0.0
1.0

WS

1.0e+2WS

f aero0.8

1.0e+10.6

τ total
0.4

0.4

0.2

0.2

τ aero

0.0

0.0

1.0

1.0

HS

HS

WS
WS

1.0e+01.0e-11.0e-2OCOC

1.0e-30.8

HSHS

1.0e-40.6

0.6

τ totalfaero

Optical Thickness

0.8

f aeroSize Distribution

0.6

faero

Optical Thickness

0.8

size distribution

0.2

τ aero

0.4

0.4

0.2

0.2

τ aero

0.0

0.0
0.4

0.5

0.6
0.7
Wavelength (μm)

λ

0.8

0.9

1.0e-51.0e-6
1.0e-31.0e-21.0e-11.0e+0Radius (μ m)radius of ptl.(μm）

1.0e+1

N.B.

Angstrom exponent ( α )α = − ln(τ λ1 / τ λ 2 ) / ln(λ1 / λ 2)

Spectral aerosol optical thickness areused to retrieve the Angstrom exponent
and particle size distribution :size of aerosol ～ 1 / αDiffuse radiation field
Diffuse radiation arises from the light that
undergoes one scattering event (single
scattering) or many (multiple scattering).
First order scattering
Second order scattering
Third order scatteringτ∗Radiative transfer equation
describes the diffuse radiation field: ref. S.Chandrasekhar "Radiative Transfer"1∂Iμ= I (τ , Ω) −P(Ω, Ω′) I (τ , Ω′) dΩ′
∫
∂τ
4π 4π
Satellite observes the upward radiation at the top of
atmosphere:

I* (τ=0,Ω)τ∗desiredvaluesChange of coordinate for multiple scattering
calculations

incident lightscattered lightcosΘ = cos θ0 sin θ m cos θ sin θ0 cos (φ - φ 0 )To get the numerical values ofI* (τ=0,Ω) for aerosol remote sensing,
Radiative Transfer Equation is solved
based on the adding-doubling methodwith boundary conditions:example :solar light incidence
Lambert groundI − (0, Ω) = π F0 δ (Ω − Ω 0 )I (τ , Ω) = A / π ∫ I (τ , Ω′)μ ′dΩ
+

−

2π

Adding-doubling Method

S (τ*)I * (τ*)satellite aerosol retrieval

contents

1.
2.

Introduction
Satellite aerosol remote sensing
2.1 Basic concept
2.2 Light scattering by aerosols

2.3 Satellite aerosol retrieval
3.

4.

Validation of aerosol remote sensing
3.1 Ground-based sun photometry
3.2 Model simulation
Future satellite sensors

Algorithm for aerosol retrieval• Total intensity– two channel method over ocean (Red, NIR)
• AVHRR– four channel method over ocean (Vi, Bl, Red, NIR)
• GLI, (SeaWiFS, MODIS .... )– dark target method over (veg.) land (Bl, Red, SWIR)• MODIS– deep blue method over bright target (desert) (Vi, Bi)• MODIS– UV method over land and ocean (UV)• TOMS, OMI• Polarization– two channel polarization method over land (Red, NIR)
• POLDER

Retrieval algorithm for aerosols (over ocean)

1. Four-channel algorithm(by Higurashi and Nakajima):– Retrieval of τ, α, and absorptivity in blue
over ocean:– 0.412, 0. 443, 0.670, and 0.865 μm(Violet, Blue, Red, NIR)
– Species; soil dust, carbonaceous, sulfate, and sea salt

Retrieval algorithm for aerosols (ocean/land)
2. Two-channel method (by R. Hoeller et al. );– Retrieval of τ and ω on a global scale(land and ocean)– 0.380 and 0.412 μm(UV-absorbing and Violet);Species； soot and dust (absorbing) andsulfate (non-absorbing)

Retrieval algorithm for aerosols (land)

3. Two-channel Polarization method- Retrieval of τ, and α, on a global scale- 0.678 and 0.865 mm(Polarization and Directionality)
4. Two-channel Polarization method with
UV-V channel method

2.3.1 Aerosol retrieval for POLDER-1& -2

Example of aerosol retrieval• Satellite data
– POLDER on ADEOS-1 (1996 to 1997)– POLDER-2 on ADEOS-2 (2003)– POLDER-3 on PARASOL (2005- in operation)– developed by CNES (France)– multi-angle viewing (up to 14 dir. = 14 scat. ang.)– polarization (Q, U) at 0.44, 0.67 and 0.87 µm– large FOV ( global obs. / 1 day)– coarse resolution 6 by 7 km2 at nadir

Processing line for POLDER
Land
surfacemodel

Aerosol
model

Molecular
information

Satellite data

Comparison between satellite andtheoretical value

R.T. calc.
TOA
Ref. DBAtmosphere-surface model
Molecules

AltitudeAerosols

land surface

sea-surface

Soil
Vegetation

BPDF w/ IGBP & NDVI

Water

BPDF w/ Wind speed

Model description

Aerosol models• Spherical aerosols• Size distribution (Bi-modal log-normal)– Modal radius of fine and coarse are chosen fromaerosol climatology based on AERONET (Dubovik,
2002).

– Fine mode (rg f ,ｌｎ σf) = ( 0.135, 0.430 µm )• Urban, Industrial and Biomass burning aerosols– (GSFC, Paris, Mexico, and Maldives)– Coarse mode (rg c , ｌｎ σ c) = (2.365, 0.630 µm )• Dust and Oceanic aerosols– (Bahrain, Solar-Villeage, Cape Verde, and Lanai Island)• Complex Refractive Index (n, k)– 1.40-0.000i, 1.45-0.0005i, 1.50-0.001i, 1.55-0.010i

Sensitivity of aerosol properties• Based of Mie calculation
λ : 0.670 µm, 0.870 µm
Size distribution： Single model log-normal （rg, σ)
rg : 0.05, 0.1, 0.2, 0.4 µm
σ : 2.0 µm (fixed)

Refractive index (m)sulfatedust
Real : 1.40, 1.45, 1.50, 1.55
Imag: 0.0, 0.0005, 0.001, 0.002, 0.004
Dust aerosols by AERONET Ref.Img = 0.001 @ 0.87µm

Sensitivity for size
(rg= 0.05, 0.1, 0.2, 0.4 µm, σ =2.0 µm (fixed))
m=1.45-0.0i (fixed)
0.67 µm

0.87 µm

Sensitivity for Real part of Ref. Idx
Size (Fixed): rg= 0.1 µm, σ =2.0 µm
rfr=1.40, 1.45, 1.50, 1.55, rfi = 0.0 (Fixed)
0.67 µm

0.87 µm

Sensitivity for Imag part of Ref. Idx
Size (fixed) : rg= 0.1 µm, σ =2.0 µm
rfr=1.45 (fixed), rfi = 0.0, 0.0005, 0.001, 0.002, 0.004
0.67 µm

0.87 µm

Aerosol retrieval over the ocean

radiance & polarization degree
at 0.670 µm and 0.865 µm

optical thickness (τ)Ångström exponent (α)
refractive index (m; real part)Aerosol retrieval over ocean
Intensity

Degree of Polarization

Aerosol retrieval over land

Intensity
LambertiansurfaceLargevariability: Albedo
dependent

Polarization

Lambertiansurfacenovariability: Albedo
independentAerosol retrieval over landpolarized radiance
at 0.670µm and 0.865µm

optical thickness (τ)Ångström exponent (α)

Surface modelover desert

over forest

Relationship between polarized reflectanceat wavelength of 0.670 µm and that of 0.865 µm

2.3.2 Retrieved aerosols from POLDER-1

Retrieved aerosol distribution over Japan
Aerosol optical thickness

March 18, 1997

April 25, 1997

Angstrom exponent

Retrieved global distribution of AOT(τ)

Nov. 1996
0Feb. 1997

0.5

1.0

Jun. 1997

Retrieved anthropogenic aerosols on a global scaleAerosols over East Asia : November, 1996

2.3.3 Retrieved aerosols from POLDER-2

Retrieved results derived from POLDER-2 data

1. Aerosol Events# Dust event on April 13, 2003
# Bio-mass burning on May 21, 2003
2. Global map in April, May, and June
3. Comparison of global map in AMJ, 1997
with that, 2003

Apr.13, 2003

Shirahama

0

τ

α
0.5
1.0

1.0
2.0

May 21, 2003

Shirahama

τ

α

0

0.5
1.0

1.0
2.0

Inter-annual change in AOT (1997-2003)

Inter-annual change in AOT (1997-2003)

April
0.0

0.1

May
0.2

0.3

June

Contents

1.
2.

Introduction
Satellite aerosol remote sensing
2.1 Basic concept
2.2 Light scattering by aerosols
2.3 Satellite aerosol retrieval

3.

Validation of aerosol remote sensing
3.1 Ground-based sun photometry

4.

3.2 Model simulation
Future satellite sensors

3.1.1. NASA/AERONET

Validation of aerosol remote sensingAerosol robotic network

World wide sun photometer network

200 sites
world wide

3 sites in Japan
Shirahama （ 2000～ ）Osaka（ ’02, 2004～）
Noto（ ’01, ’03, 2007～）

Instrument
Cimel CE-318 sun/sky radiometer
0.34, 0.38, 0.44, 0.50, 0.67, 0.87, 0.94, 1.02 µm（ +polarization，+1.64 µm etc.）
Power：
12V Battery with solar panel
Controler：
Water proof box
Data logger： Sat. Trans. GOES，Meteosat, GMS）

Calibration
1st CalMaint

2nd Cal Level 2 QA dataSwap out package

sensor headcollimatorcable
connectors brd.
control box

AOT measurements at OsakaData quality
Quality assured AOT±0.01 （all instruments）±0.003（master inst.）

Calibration for direct light
Master instruments
calibrated at Mauna Loa Observatory
OthersInter calibration (trans. master to others)
Inter cal. at GSFC
Calibrations for diffuse light
Integrated sphere (GSFC，LOA etc.)

Level 2 (quality assured) data
Calibration A

2006

Calibration B

2007

2008

L2
L1 / L1.5
L1
L1.5
L2

Cal. B w/o cloud screening
Cal. B w/ cloud screening
Cal. A/B w/ cloud screening and human checkValidation of POLDER resultswith sun photo (AERONET) data
Eleven AERONET sites (Aire Adour, Banizoumbou, Bidi Bahn,
Bondoukoui, Dakar, Dalanzadgad, GSFC, Los Fieros, Mfuwe, andZambezi) are selected for validating the retrieved results fromPOLDER-1 data according to the following rules:
1) The AERONET measurements are selected within the +/- 30
minutes over flight ADEOS-1 satellite.
2) The values of AOT at wavelengths of 0.443, and 0.870 µm as
ground based measurements are selected for calculating Angstrom
exponent.
3) The AOT at wavelength 0.550 µm is estimated based on the
Angstrom exponent and the measurement of 0.670 µm.
4) The POLDER results are selected within the ~20km (3x3 POLDER
grid) square around the AERONET site.

Validation of retrieved results over land

Angstrom

Cimel (AERONET)AOT (0.55 µm)

POLDER

3.1.2 Aerosol properties
from MODIS Level 2 product (MYD04)

MODIS Aerosol productMOD04 (Terra) , MYD04 (Aqua)– L2 scene (10 by 10 km2)
– L3 monthly product– AOT over ocean and land
– other aerosol properties are also available.
http://modis-atmos.gsfc.nasa.gov/MOD04_L2/index.html

Aerosols by MODIS/Aqua in 2006
Monthly mean of AOT (0.55 µm) (w/ deep blue algorithm)

0

Angstrom exponent (w/ deep blue algorithm)

0.5
1.0

1.0
2.0

AOT
Ang.

Validation of MODIS AOT (Osaka,Japan)

Contents

1.
2.

3.

Introduction
Satellite aerosol remote sensing
2.1 Basic concept
2.2 Light scattering by aerosols
2.3 Satellite aerosol retrieval
Validation of aerosol remote sensing
3.1 Ground-based sun photometry

3.2 Model simulation
4.

Future satellite sensors

Aerosol Study
forecast

evaluation
observation①

spacebased②

groundbased③

model simulation

Aerosol simulation model
SPRINTARS(Spectral Radiation-Transport Model for Aerosol Species)[Takemura et al., 2000; Takemura et al., 2002; Takemura et al., 2005]

combine with CCSR/NIES/FRCGC AGCM5.7.
resolution: T42 and 20 levels
aerosol species

zsulfate: SO2(fossil fuel, biomass burning, volcano),
DMS(Calculated by surface downward short wave radiation flux andsurface skin temperature and land vegetation)

zcarbone（BC，OC）: BC and OC (fossil fuel, biofuel,agricultural activity,biomass burning), OC (terpen)
zdust:Calculated by wind speed at 10m, soil water, snow amountand land vegetation.

zoceanic:Calculated by wind speed at 10m.
aerosol transport processes:
emission, advection, diffusion, chemical reaction and deposition.
courtesy of Dr. M.Mukaiex) simulated aerosol emission in 1850

courtesy of Dr. M.Mukaiex) simulated aerosol emission in 2000

courtesy of Dr. M.Mukai

Contents

1.
2.

3.

4.

Introduction
Satellite aerosol remote sensing
2.1 Basic concept
2.2 Light scattering by aerosols
2.3 Satellite aerosol retrieval
Validation of aerosol remote sensing
3.1 Ground-based sun photometry
3.2 Model simulation

Future satellite sensors

Future aerosol sensors (JP)• CAI on GOSAT in Jan., in 2009 (JP)0.38, 0.67, 0.87 and 1.6 µm for aerosols/ clouds• APS (polarimetry) on GLORY in 2009 (US)multi wavelength polarimetry for aerosols• VIIRS on NPP （2010) and on NPOESS (201x) (US)• SGLI (polarimetry) on GCOM-C in 2013 (JP)two wavelength polarimetry for aerosols

Polarization measurements with SGLI
on GCOM-C1 (in 2013)• Stokes parameter
I, Q, U
(0 – 60 –120 position angles)• Visible and near-infrared channels
0.670 and 0.865 µm ( similar to POLDER chls )• Large tilting angle±45 degrees about nadir in the along-track
direction• Fine resolution 1 km (nadir)• FOV~1000 km swath (cross-track)L5. Satellite Remote Sensing byTemperature and Humidity
Sounders

Kozo Okamoto

Numerical Prediction Division, Forecast Department,
Japan Meteorological Agency

The Eighteenth IHP Training Course
Satellite Remote Sensing of Atmospheric Constituents
3-15 November 2008
Nagoya, Japan

Outline
1. What is sounder?
2. Example of sounder data
3. Principle of sounding
4. Retrieval process and products
5. Application to data assimilation
6. Summary
7. References

p.2

p.3

1. What is sounder?

What is “sounder”?
“Sounding” is the process to derive (vertical)
distributions of temperature, atmospheric constituents(e.g. humidity, ozone,,,) , aerosols, and hydrometeors(clouds/precipitations)This lecture focuses on sounding of temperature andhumidity (T & H) using passive sensors
 Ref: Active sensors, such as radar and lidar, alsomeasure high-resolved vertical distribution of parameters
above.

Sounders are passive sensors aiming at sounding
profiles of, mainly, T & H.

p.4

What is “sounder”? (cont.)
3 types of T & H vertical soundingby passive sensors
 (1) nadir sounding Satellites measure upward radiationfrom the atmosphere and surface
 Extract vertical distribution
information from radiations at anumber of wavenumbers
 Fine horizontal resolution (10~km)
but poor vertical resolution (1~5km),
compared with limb sounding
 e.g. AMSU, HIRS, AIRS, IASIWF (Weighting Function) :the contribution each atmospheric layer andsurface make to the radiation measured

p.5

WF of nadir sounder (AMSU-A)p.6

What is “sounder”? (cont.)
 (2) zenith sounding (from groundstation)WF of zenith sounderWestwater et al.

 Ground-based sensors measuredownward radiation from the atmosphere Extract vertical distribution informationfrom radiation at a number ofwavenumbers
 Hard to extract vertical-resolved
information due to the maximum
sensitivity to the atmosphere adjacent to
the groundWF of limb sounder (3) limb sounding Satellites measure atmospheric radiation,

sunlight, starlight and radio wave thatcross atmosphere Extract vertical distribution information
using many paths of them Fine vertical resolution (1-2km) but poor
horizontal resolution (>100km) comparedwith nadir sounding.
 No surface influence e.g. ENVISAT/MIPAS, Aura/MLS, GPSradio occultation

Gille and House,
1971

Nadir sounding in the IR/MW region
Commonly used for operational meteorological observations
IR (infrared) sounders
 Less variable surface emissivity, higher spatial resolution
 Sensitive to clouds => cannot see inside and under clouds
 NOAA/HIRS(20ch), GOES/SOUNDER(19ch),

FY3/IRAS(20~26ch)
 Hyper-spectral sounders, such as FTS (Fourier Transfer
Spectrometer), provide higher spectral resolution data Higher vertically resolved information can be derived

 Aqua/AIRS(2378ch), Metop/IASI(8461ch)

MW (microwave) sounders
 Less sensitive to clouds => much wider observation coverage Lower horizontal resolution, more variable surface emissivity
 NOAA/AMSU-A(15ch), AMSU-B(5ch), MHS(5ch),

FY3/MWTS(4~17ch), MWHS(8ch), DMSP/SSMIS(24ch)

ATOVS=HIRS + AMSU-A + AMSU-B (or MHS)

p.7

p.8

WF of IR soundercomparison of traditional IR sounder HIRS (20ch) withhyperspectral IR sounder AIRS (2378ch)
 Longwave CO2 band
WF (HIRS ch1~7)

WF (AIRS)

comparison of spectral resolution of HIRS & AIRS

p.9

Ch characteristics & WF of AMSU-A
AMSU-A channel characteristics and specification
chCenter Frequency

Bandwidth
[MHz]

NET
[K]

Polarization
at nadircharacteriz
ation

1

23.800 GHz

270

0.30

V

window

2

31.400 GHz

180

0.30

V

window

350.300 GHz

180

0.40

V

window452.800 GHz

400

0.25

V

T sounding

5

53.596 GHz  115MHz

170

0.25

H

T sounding

6

54.400 GHz

400

0.25

H

T sounding

7

54.940 GHz

400

0.25

V

T sounding

8

55.500 GHz

330

0.25

H

T sounding

9

57.290 GHz (=f0)

330

0.25

H

T sounding

10

f0217MHz

78

0.40

H

T sounding

11

f0322.2MHz48MHz

36

0.40

H

T sounding

12

f0322.2MHz22MHz

16

0.60

H

T sounding13

f0322.2MHz10MHz

8

0.80

H

T sounding

14

f0322.2MHz4.5MHz

3

1.20

H

T sounding

15

89.000 GHz<6,0000.50

Vwindow

WF (AMSU-A)
A14

A3

Ch characteristics & WF of AMSU-B & MHS

p.10

Altitude of the WF peak for H-sounding is more variable
than for T-sounding depending on absorber amount.
 Increasing absorber makes WF peak higher.
AMSU-B channel characteristics and specification
chCenter Frequency

Bandwidth
[MHz]

NET
[K]

Polarization
at nadircharacteriz
ation

1

89.00.9 GHz

1000

0.37

V

window2

150.00.9 GHz

1000

0.84

V

window3

183.311.0 GHz

500

1.06

V

H sounding4

183.313.0 GHz

1000

0.70

V

H sounding5

183.317.0 GHz

2000

0.60

V

H sounding

MHS channel characteristics and specification
chCenter Frequency

Bandwidth
[MHz]

NET
[K]

Polarization
at nadircharacteriz
ation

1

89.0 GHz

2800

0.22

V

window2

157.0 GHz

2800

0.34

V

window3

183.311.0 GHz

2x500

0.51

H

H sounding4

183.313.0 GHz

2x10000.40

H

H sounding5

190.31 GHz

2200

0.46

V

H sounding

WF (AMSU-B)
tropical conditions

polar conditions

O2 & H2O absorption lines in MW region
Bands where absorption is relatively strong and changes
with frequency are used for T/H-sounding.
absorption

O2
H2O

40

80

120

160

200
frequency

p.11

IASI spectra in the IR regions

p.12

First IASI observation of brightness temperature (TB)
Bands where TB gradually changes, or absorption changes, aremainly used for T/H-sounding.

http://www.eumetsat.int/Home/Main/Access_to_Data/Metop__amp__NOAA_Services/Global_Data_Service/SP_1119366824888?l=enp.13

Scanning systems
Cross-track scan
 View angle varies to get wide swath
 ATOVS, AIRS, IASI, CrIS, ATMS
 Note spatial resolution degrades towards the edge
 For AMSU-A, 48km at nadir and 79X149 km at edge

Conical scan
 View angle is fixed and rotates around the nadir direction
 Spatial resolution does not vary with view angle

 SSMIS, MIS

cross-track scan of AMSU

http://www.meted.ucar.edu/npoess/microwave_topics/land_ocean/print.htm

conical scan of SSMI

p.14

2. Example of sounder datap.15

2-1. IR sounder : AIRS
Analysis T500

Brightness temperature (TB) of AIRS ch
168,221 and 787
As WF goes down, TB increases in the
troposphere
Window ch reflects SST

Analysis T1000

 Highest TB (if inversion does not exist)

because of surface emissivity is almost 1.0

Upward radiation is absorbed by clouds Lower TB in cloudy areas than in clear areas
AIRS168 14.33m 200hPa

AIRS221 14.03m 600hPa

AIRS787 10.90m window

p.16

IR sounder : AIRS (cont.)
limb darkening and brightening Inherent in cross-track scan sounders
 TB decreases toward scan edges in the

troposphere where temperature is down withaltitude TB increases toward scan edges in thestratosphere where temperature is up withaltitude
AIRS168 14.33m :200hPa

limb darkening

edge

nadir

AIRS72
AIRS7214.99m
14.99m:50hPa:50hPa limb
limbbrightening
brightening

p.17

2-2. MW sounder : AMSU-A
TB decreases (increases) with altitudes in the troposphere(stratosphere), as IR sounders
AMSU-A3 :1000hPa

However, TB of
window channel is
not highest over thesea because of low
emissivity
Analysis T30

AMSU-A4 :800hPa

AMSU-A5 :600hPa

AMSU-A6 :400hPa

AMSU-A8 :200hPa

AMSU-A9 :100hPa

AMSU-A11 :30hPa

AMSU-A12 :10hPa

AMSU-A14 :3hPa

Analysis T3

p.18

MW sounder : AMSU-A (cont.)
TB increases in areas with dense water vapor and cloud overthe sea where the surface contribution is small unlike IR
observations
AIRS787 :surf
Limb darkening/brightening can be seenas IR sounder However, surface sensitive channels in

MW can show limb brightening in the
troposphere too.

AMSU-A1 :surf,TPW

AMSU-A2 :surf,TCCLW

AMSU-A3 :surf

p.19

3. Principle of sounding

Conditions of (simple) temperature sounding

p.20(1) The emitting/absorbing constituent should be uniformly
mixed (constant mixing ratio with altitude)(2) The absorption strength should be appropriate(3) The absorption band in question should not be overlapped bybands of other constituents(4) LTE (local thermodynamic equilibrium) should apply
These conditions determine the absorber gas, wavenumber(frequency) and target altitude range for temperaturesounding

CO2:15 & 4.3m, O2:60,118GHz
O2:60GHz100km, CO2:15m80km, CO2:4.3m35km

Humidity sounding usually uses

H2O:6.3m, 183GHz

The stronger absorption, the higher WF peak.

p.21

Bell-shape of WF
Why WF has a peak at a particular
altitude?
 Lower atmosphere Strong radiation is emitted becauseof high atmospheric density
 But, almost all is absorbed by theupper atmosphere Upper atmosphere Little radiation is absorbed by the

upward radiation fromthree heights in theatmosphereprofiles of contribution
to radiance at TOATOA : top of atmosphere

D

M

U

U

further upper atmosphere But, little radiation is emitted
because of low density.

M As a result, radiation from theatmosphere at an intermediate
altitude has a maximum
contribution.

D

Eyre (1991)

p.22

What is WF? (1)Radiative transfer model for upward radianceI  I s  I air  I s  I up  (1   s )Trs I down
I s   sTrs B(Ts ) :upward radiancefrom the surface
I up s

0

I down B(T )Τ r ( )d  
s



0

s



0

B(T ) dr d : upward radiancefrom atm ospheredΤ

B(T )Τ r ( s   )d  B(Tc )Trs 

s



0

Τ

B(T ) Τrs d  B(Tc )Trsr

: downward radiancefrom atm oshpereand cosm icback ground,where , T, Tc, B(T) are emissivity, atmospheric temperature, cosmic
background temperature (~2.7K), and Plank function.  and Tr( ) are
optical depth and transmittance from the satellite or top of
atmosphere (TOA). Suffix s indicates the surface (to TOA).

Tr  exp  Weighting function WF, when s=1, is described by0 d ( z )dr ( )d ( p)d  B r
dp  B rdz  B WF( y)dy
s
ps
0
d
dp
dz
d ( z ) The most familiar form is
WF( z )  rdz
 WF determines contribution of B(T) at each layer to radiation at TOA

I air  I up 



0

B







p.23

What is WF? (2)WF=dTr /dz
WF has peak where Tr varies mostwith respect to altitude (right fig)
 The greater Tr change, the shaper WF

Transmittance and WF

0100

Another perspective:dTr dTr d Tr  kdzd dz
 Tr  exp   , d  kdz

WF 

200
300
400
500

 k : absorption coefficient600  : absorber density

700

WF has a peak where conflictingchanges in Tr and k are balanced.
 As altitude increases, transmission(Tr) generally increases and absorber() decreases.

800

transmittance Tr

900weighting function
W

1000
0.00

0.20

0.40

0.60

0.80

1.00

p.24

What is WF? (3)General weighting function takes Idown into accountI  I s  I air  I s ' I air 'I s '  I s  (1   s ) B(Tc )Trs ,2

I air '  I up  (1   s )Trs I down
2

s
Τ 
Τ
Trs I down  Trs  B(T ) rs d   B(T ) rs  Τ r d00Τr
 Τr 
s

2

2


Τ 
 Τ  dΤ r  B(T ) rs  dΤ r   B(T ) rs 
dz
Τ rs
0
 Τr 
 Τ r  dz
1

2


 Τ rs   dΤ r 
 I air '   B(T )1  (1   s )
dz   B(T )  WFdz
00 Τ r   dz
As a result, general weighting function GWF is2

 Τ rs  dΤ rGWF  1  (1   s )
 Τ r  dz
However, we hereafter consider a simple WF ( GWF, for clearer perspective.

dΤ r), notdz

p.25

Simple models of WF[1] Nadir sounding (for a single absorption line)[2] Zenith sounding (for a single absorption line)[3] Nadir sounding for a non-monochromatic band
WF of nadir sounding in the pressure coordinate
dTr dp dTr
p dTr

 
,
(1)
dz
dz dp
H dp
z
dp
p
where p  p s e H ,dzH p:pressure, ps:surface pressure,
H:scale heightWF 

optical depth 




zT

z

TOA

z p  Trsurfacerr pkdz   k dp 
kdp (2)
p g
0
g



0



 k:absorption coefficient,zT 0 0 1

:absorber density,

r:mixing ratio of absorber, g:acceleration of gravity,zT :satellite altitude at TOA (p=0)

0 ps  s Trs

Simple model of WF [1] : Nadir sounding

p.26

Estimate  for a absorption gas with constant r with altitude Now we select observation wavenumber  in the line wing region withlarge -0. Absorption coefficient with the Lorentz line shape becomesk0 pk k0 p(3)(  0 ) 2  k1 p 2
 Insert (3) in (2),k0 r pk0 r 2
p2

pdp 
p 
,
(4)
2

0
g
2g
pm
where pm2=2g/k0r.

Transmittance Tr is derived by (4)Tr  e   exp   p pm 2

(5)

WF is finally obtained using (1) and (5),







 2p  2  p 
p
2 
 
  exp   p pm 2
WF    exp   p pm   
 p 2  H  pm 
H


m 

2

 (6)

 Increase in absorber and decrease in transmission with p generate thebell-shape structure.

Peak altitude of this WF is pm by dWF/dp=0

p.27

Simple model of WF [1] (cont.)
examples of WF
 peak position : pm=50, 200, 400 and 800hPa

linear pressure coordinate

log pressure coordinateSimple model of WF [2] : Zenith sounding
Let’s consider WF in the line wing for zenith sounding
Tr for zenith sounding is described by (4)Tr  e ( s  )  e  s  exp  p pm 2



(5)'

WF is obtained, as (6), from (1) and (5)’.
2



 p 
2
 exp  p pm 2
WF  e  s 

H
p
m





(6)'

 Increase in both absorber and transmission with p cannot

generate the bell-shape structure, but a maximum at surface.

p.28

Simple model of WF [3]:p.29

Nadir sounding for non-monochromatic band
The previous models [1] & [2] have been based on a single
absorption line.
Because, however, real instruments have a wide spectral
response function, many lines should be considered.
Tr for a number of absorption lines can be calculated using astrong Elsasser band model.
Tr  1 


 

2

'p

e  x dx  1  erf ( ' p)2(5)' '

0erf ():error function, ’ :constant anddepends on the local line strengths

WF is given by differentiating (5)’’with respect to p, as (1)
WF 

1
H



2 p
exp   p 2 pm 2 pm(6)' '

WF is broader than one from wing of asingle absorption. (see right figure)

Comparison of WF shape of [1](mono)and [3](nonmono):p.30

4. Retrieval process and products

Retrieve T/H profiles
RT equation : y=H(x) x: temperature/humidity profiles we want to infer

 y: radiance observation of all channels
 H: radiative transfer (RT) model

Retrieval process: x=H-1(y) This is called “inversion problem”
 “forward problem” is to calculate y from x (RT calculation itself)

Methods of retrieval
 Statistical method: x=Cy a means deviation from the average of a
 C can be obtained from training dataset: a match-up of radiosondes(xs) and corresponding radiances (ys) xs=C ys => C=xs ys T(ys ys T)-1
 Computationally cheap and need no RT calculation nor first-guess, butvulnerable to sampling error
 MAP (maximum a posteriori) method * Almost equivalent to ML (maximum likelihood) method
 More sophisticated: hybrid of statistical and physical method
 Not need huge training dataset, more stable and easier extension
 But need a RT model and first-guess

p.31

p.32

Retrieval process : MAP method
Estimate the state that maximizes conditional probability p(x|y)
 p(x|y) means the probability that x will occur when y occurs (ormeasurements are given)

Bayes theorem and the assumption of Gaussian probability givesp( x | y )  p( y | x)  p ( x) / p ( y )exp  1 2 x  x  B x  x  exp  1 2 y  H ( x) R 1 y  H ( x) 
TT

Probability density functionp(x|y)

1bbp(y|x)

xb: a priori state or background profile(first-guess) e.g. NWP, climate, nearby radiosondep(x)profiles,,, B: error covariance of xb R: error covariance of observationy and its operator H

xb

x

y

p.33

MAP method (cont.)
This problem is equivalent to estimating the state thatminimizes the cost function J(x)J(x)=-ln{p(x| y)}=1/2(x-xb)TB-1(x-xb)+1/2[y-H(x)]TR-1[y-H(x)] This is also called variational problem.
 1D-Var : x is one dimensional variable (e.g. vertical T/H profile)

The optimal solution of xa (the most probable profile) is
obtained by dJ(x)/dx=0xa  xb  W y  H ( xb )   xb  BK KBK  R
TΚ is linearized H(x) : y-H(xb )  K  (x-xb )Error covariance of retrieval xa1A K B KRT  I  WK B

1 1

T

  y  H ( x )1b

Other retrieval processes
Pre-processing needed for T/H-retrieval
 cloud screening for IR T/H-sounding rain screening for IR/MW T/H-sounding
 limb correction for cross-track scan sounders (if necessary) bias correction (if necessary) create first-guess (if necessary) ,,,,

Various products, in addition to T/H profiles, can be derived

p.34

Products of operational sounders

p.35

EUMETSAT : European Organisation for the Exploitation of Meteorological Satellites
 Metop/ATOVS products
 T/H profiles at 40/15 pressure levels, Ts, CTH, CTT, CC, TCCLW, TPW
 http://www.eumetsat.int/Home/Main/What_We_Do/Highlights/SP_121
8437114837?l=en
 Metop/IASI products
 T/H profiles, Ts, CTH, CTT, CC, cloud phase, integrated ozone, total
column of N2O, CO, CH4 and CO2
 http://www.eumetsat.int/Home/Main/Access_to_Data/Metop__amp__
NOAA_Services/Global_Data_Service/SP_1119366824888?l=en

NOAA : National Oceanic and Atmospheric Administration
 GOES SOUNDER products
 TPW, Ts, CTH, CAPE, LI
 NOAA/ATOVS products
 MSPPS : Microwave Surface and Precipitation Products System
 TCCLW, TPW, RR, sea ice concentration, ice water path, snow cover,land Ts, snow water equivalent, surface emissivity
 http://www.star.nesdis.noaa.gov/corp/scsb/mspps/main.html

CTH:cloud top height, CTT:cloud top temperature, CC:Cloud Cover, RR:rain
rate, TCCLW:total column cloud liquid water, TPW:total precipitable water
CAPE:convective available potential energy, LI:lifted index

Examples : Products from Metop/ATOVS & IASIEUMETSAT products on 11. July 2008
 Both ascending and descending orbits data
Specific Humidity [g/kg] at 500hPa (left), Temperature [K] ant 500 hPa (center) and at
200hPa (right) from ATOVS

columnar amountsof CO in 1018
molecules/cm2,
from IASI

p.36

Examples : Products from NOAA16/ATOVS

p.37

NOAA/NESDIS operational product Bias and RMSE of ATOVS temperature/humidity sounding

retrieval (heavy lines) and first-guess (light lines)
 NOAA-16; September 2001
T : clear conditions[K]

T: cloudy conditions[K]

H: 30N-30S[%]

http://www.star.nesdis.noaa.gov/smcd/opdb/poes/docs/

Examples : MSPPS Products from ATOVSNOAA MSPPS products on 22 Sep. 2008 from ATOVS http://www.star.nesdis.noaa.gov/corp/scsb/mspps/realTime_n18.html

TPW

TCCLW

Snow
Cover

Ts

p.38

Tools for retrieval

p.39

Pre-processing package
 AAPP The ATOVS and AVHRR Processing Package

Radiative transfer model RTTOV : Radiative Transfer for TOVS http://www.metoffice.gov.uk/research/interproj/nwpsaf/rtm/index.html
 CRTM : Community Radiative Transfer Model http://www.jcsda.noaa.gov/projects_crtm.php
 SDSU : Satellite Data Simulator Unit
 http://precip.hyarc.nagoya-u.ac.jp/sdsu/sdsu-main.html

Retrieval package
 IAPP : International ATOVS Processing Package
 1D-Var: e.g. NWP SAF (organized by EUMETSAT) http://www.metoffice.gov.uk/research/interproj/nwpsaf/1dvar/index.ht
ml NWP SAF : Numerical Weather Prediction Satellite Application Facility

ref : http://cimss.ssec.wisc.edu/itwg/sssp/

p.40

5. Application to data assimilation

What is Data Assimilation (DA)?
Data Assimilation (DA) Blend observations and numerical models to create morereliable state using the theory of statistical estimation
 Accurate observations can still have errors and are

irregularly distributed in time and space Models based on physical laws can still have inaccuracy.
 E.g. Improve models using reliable observations
 E.g. Estimate 3D/4D state from limited observations

using physical models

DA is applied to data integration to understandthe earth system and creating initial conditions of
NWP (numerical weather prediction)

p.41

p.42

Flow of NWP DA

Time
Prediction
Observations

Pre-processing

Main Analysis
analysisgenerating
first-guess orlong-range forecast

First-Guess

Observations

Main Analysis

Pre-processing

analysis

Prediction
generating
first-guess orlong-range forecast

First-Guess
Prediction
Observations

Pre-processing

Main Analysis
analysisgenerating
first-guess orlong-range forecast

Data used in the JMA global DA system

p.43

SYNOP, Ship, Buoy

Radiosondes and Wind Profilers

Aviation, Australian BOGUS

Atmospheric Motion Vector

T-Sounder (AMSU-A)

MW Scatterometer (SeaWinds)

WV ch radiances of geo.sat. imagers

H-Sounder (AMSU-B, MHS)

MW imager (SSMI,TMI,AMSR-E)

Satellite data operationally used in JMA

p.44

Imager AMV: Atmospheric Motion Vector (G/M)
 MTSAT-1R, GOES-11,12, Meteosat-7,9satellite/sensor

 Aqua/MODIS, Terra/MODIS

 (WV-ch) radiance (G) MTSAT-1R, GOES-11,12, Meteosat-7,9MW scatterometer

G: global analysis
M: meso-scale analysis

 Ocean surface winds from QuikSCAT/SeaWinds (G/M)

GPS radio occultation (RO)/ground station
 Reflactivity (T/H) from GPS-RO (G) CHAMP/BlackJack、GRACE/Black Jack
 Zenith tropospheric delay from GPS ground based station (M)MW imager Radiance (G), TPW and RR (M) DMSP-13,14/SSMI, TRMM/TMI, Aqua/AMSR-E

Sounder Radiance (G) and Temperature profile (M) NOAA-15,16,17,18/ATOVS, Aqua/AMSUA, Metop/ATOVS

p.45

Analysis method
Typical analysis method
 OI (Optimum Interpolation) : x  xb  BK T KBKT  R y  H ( x )1b

 Used by many operational NWP centers in 1980s-1990s
 Assimilate only observations linearly related to analysis variables
 For sounders, assimilate T/H retrievals, not radiances

 3D-Var (3-Dimensional Variational) J(x)=1/2(x-xb)TB-1(x-xb)+1/2 {y-MH(x)}T R-1 {y -MH(x)} 4D-Var : extend 3D-Var in time dimension
 Currently adopted by many operational NWP centers KF (Kalman Filter) : x  xb  BKTKBK  R y  H ( x )
T

1

b

 B is updated using linearized forecast model.

 EnKF (Ensemble KF) : Merge KF and ensemble prediction

systems

4D-Varp.46

J(x)= JB (x) +JO (x)= 1/2(x-xb)TB-1(x-xb)+1/2[y-H(x)]TR-1[y-H(x)]JB: background term, JO:observation term JO = JO1 +JO2+,,, : sum of independent observations

x :analysis variables (T,Q,U,V,Psrf,,,) in 3-dimension
xb : background or first-guess. Usually use short-range (e.g 6hour) forecast from previous analysis
y :all observations selected by pre-processing systems
H: observation operator projecting analysis variables into
observation variables
 Interpolation of analysis variables at analysis grid points toobservation points, RT model, TPW=Qdz, wind speed=sqrt(u2+v2),,,

M : forecast model
B : background error covariance (huge matrix!!)
R : observation error covariance B and R give relative weights of background and observations

Assimilation of sounder data

p.47

Sounders are essential for NWP DA
 Provide vertical information of T/H
 Installed in many satellites
 MW sounders can see under (less) cloudy conditions

Radiances are assimilated
 Advantage over the assimilation of T/H retrievals Radiance assimilation, or “direct assimilation” more accurately

extract information from observation by including RT process.
 Moreover it allows flexible data selection such as channel
selection, easier error assignment and earlier data distribution
after launch.

However, the usage is limited to “clear” radiances RT model does not include cloud/rain effects in most NWP

centers

 radiances less sensitive to land surface, coast and sea-ice
 Difficult to estimate accurate surface emissivities over theland/coast/sea-ice

Pre-processing for sounder data

p.48

Main analysis (4D-Var) processes only high-quality andappropriate data.
Pre-processing in DA system selects those data and, if
necessary, correct them.
Principal pre-processings Quality control (QC) Remove erroneous data Remove data affected by clouds/rain, land surface and sea-ice.
 Bias correction (BC) Biases depend on scan, air-mass and instrumental conditions, and cantemporally change.
 Biases come from calibration, instrumental aging, orbit drift,
inaccurate RT model,,, BC removes biases using a linear regression approach Biases are usually estimated from y-H(xsonde), y-H(xb) or y-H(xa) Thinning Reduce data to match the resolution of DA system Reduce computational costs

 Assignment and modification of observation errors

p.49

Pre-processing : example of QC
O-B: y-H(xb)Remove cloud/rain-affected radiances
Remove radiances affected by land/coast/ice-sea surface.
AIRS221 Obs before QC

O-B before QC

O-B after QC

AMSU-A4 Obs before QC

O-B before QC

O-B after QC

p.50

Pre-processing : Example of BC
Scan bias correction
 Bias is estimated by y-H(xb) averaged at each scan position

Air-mass bias correction
 Bias = a1*IWLR+a2*TCCLW+ a3*Ts+ a4*1/cos(scan)+a5*const
 IWLR=integrated weighted lapse rate Coefficients are updated at every analysis
AMSU-A scan BC : y-H(xb) as a function of scan positionch9

AMSU-A air-mass BC:
y-H(xb) before BC (left)
and after BC (right)

ch10

ch11

after BC
before BCdata numSummary
Sounders extract vertical distributions of temperature,atmospheric constituents using a number of wavenumberswith different radiative characteristics.
 This lecture mainly aimed at passive nadir T/H sounders.
 Radars, lidars and limb sounders also derive verticalinformation based on different principle.

Sounders have been long and widely used by operationaland research communities.
 Diverse retrieval methods, such as MAP method, and products

have been generated.
 Essential data sources especially for NWP Assimilation of (clear) radiances appropriately pre-processed

substantially improves accuracy of NWP.

Many new instruments are planned IR sounders: NPP/CrIS (to be launched in 2010), NPOESS/CrIS(2013), MTG/IRS (2017)
 MW sounders: Megha-Tropiques/SAPHIR(2009), NPP/ATMS(2010), NPOESS/ATMS (2013), NPOESS/MIS?, Geo.sat?

p.51

References
ECMWF training course
http://www.ecmwf.int/newsevents/training/rcourse_notes/DATA_ASSIMILATION/INVERSION_METHODS/Inversion_methods.html
Janssen,M.A. (editor), 1993: Atmospheric Remote Sensing by
Microwave Radiometr. A Wiley-interscience publication, 592 pp.
Kidder, S.Q.and T.H.Vonder Haar, 1995: Satellite Meteorology: An
Introduction. Academic Press, 466 pp.
Kalney, E., 2003: Atmospheric Modeling, Data Assimilation andPredictability. Cambridge University Press, 341 pp.
MetEd Satellite Meteorology
http://www.meted.ucar.edu/topics_satellite.php
Stephens,G.L., 1994: Remote sensing of the Lower Atmosphere:
An Introduction. Oxford univ.press. 523 pp.

p.52

Lecture 6: Geostationary
Meteorological Satellites
Atsushi Higuchi
Center for Environmental Remote Sensing(CEReS)
Chiba University, JapanThe 18th IHP Training Course“Satellite Remote Sensing of Atmospheric Constituents”
3-15 November 2008, Nagoya, Japan

Today's Contents●

Introduction of Geostationary Meteorological
Satellites platforms & Sensors:– GMS, MTSAT-1R, GOES, etc ..
– Thermal-IRs, mid-IRs (water vapor, water droplet),Visible●Applications for meteorological researches– Detection of Convective Activity (Ic), Traditionalclassification by a combination of several channels– More utilize of Geo-Satellites●

Recent Topics– Introduction of VL CEReS activity– Inter-calibration project (SAT GSICS), etc.

Advantage & Disadvantage ofGeo. Sat.
●

Advantages:– High-Time Frequent Observation: Most of Geo. Sat.

capture hourly. (nothing to compare with them !)– Basic wavelength: Thermal-IR (one or two

atmosphere's window [10-13 micron]), 7 micron(Water vapor), (3 micron: water droplet), and visible.

●

Disadvantages:– Coarse spatial resolution: 4 or 5 km in IRs, 1 km invisible.

– Indirect observation for internal-cloud: Due toprinciple of Optics (can not profiling like active
sensor)Spatial resolution(Altitude of platform)
GEO
Van Allen belt
LEO

・Polar orbit:700-800km-> Balance to gravity
・Geostational:36,000km-> Also balanced withgravityGeo-Sat. Cover globe

Spin type (old style; old-GOES,
GMS, FY2-X, Meteosat, MSG)

Meteosat

FY2-C(D),
GMS-5

MSG
(Meteosat
SecondGeneration)

New type (three-axes control;
new GOES, MTSAT-1R)

Geo-Sat operation (by in-situ
Receiving station)

Earth ;-)

1. Scanning the Earthby Geo-Sat.

Geo-Sat operation (by in-situ
Receiving station)

Space Agency

Space Agency

4. Then receive databy antenna
2. Send raw data into
3. Send specified data toSpace operational agency. Geo-Sat

Full-disk Image example (MTSAT)

IR1

VIS

Here I will show several animations
derived from MTSAT-1R

Analysis of Geo-Sat for Meteorology (1)●Simple image analysis to diagnose a target
situations– Detection of convective activity by thermal-IR– Detect low-level clouds by visible– Moving feature figure: Hovmoller diagram●

Multiple (combination) usage of several
channels in Geo-Sat.
– Combination of two thermal-IR (split window tech.)– Combination of visible and IR– Combination of water vapor and IR

Hovmoller Diagram (Equator's data)Seasonal change in Ic over Tibet(Fujinami and Yasunari, 2001)

Convective Index (Ic)
(Nitta & Sekine, 1994)
Ic= 250 - Tbb (Tbb < 250K)
Ic= 0 (Tbb <= 250K)

Use of Visible (only daytime) for thedetection of low-level cloudHard to detect clouds by thermal-IR

VisibleDetectable both channels

thermal-IR1

Amount of Cloud Map revealed by
GMS 5 SVISSR (visible; 1998-2003)

Amount of Cumulus, Cumulonimbus,
Stratocumulus Map by
GMS 5 (visible & split window)

Amount of Cirrus (Anvil) Map by
GMS 5 (visible & split window)

Amount of “Small” Cumulus Map

Utilize visible channel for the detection
orographic small cloud on Tibet(Kurosaki and Kimura, 2002)
thermal-IR

VisibleCont. (Kurosaki Kimura, 2002)

Use of Visible (2: Sato et al., 2006)- Role of Vegetation and topography on Cloud Amount -Combination Usage of Geo. Sat.
Channels (visible, thermal-IRs, WV)Example: Use of WV and thermal-IR(Ohsawa et al., 2001)

Cont.
(Ohsawa
et al.,
2001)

ISCCP CLOUD CLASSIFICATION
50

CLOUD TOP PRESSURE (MB)

180

1.3

CIRRUSCIRROSTRATUS

DEEP CONVECTION
HIGH

310
440

ALTOCUMULUS

ALTOSTRATUS

NIMBOSTRATUS

560

MIDDLE
680
800

CUMULUSSTRATOCUMULUSSTRATUS
LOW

1000

3.6

9.4
CLOUD OPTICAL THICKNESS

2360

ISCCP-D2 1983-2007
mean annual
High

MiddleLow
http://isccp.giss.nasa.gov/products/onlineData.html

Focus on time series (shape,
tracking moving clouds)

Example:
Kondo et al.,2006
- Tracking statics
over Maritime
Continent -(Count. Kondo et al., 2006)

(Count. Kondo et al., 2006)-Relation Life time vs rain rate by TRMM TMI -GSMaP (in detail, Lecture 10 byProf. Ushio)

http://sharaku.eorc.jaxa.jp/GSMaP/index.htm

32Global geostationary databy 4 Univ. VL activity

×

●

●

FY2-C

×
×
×
METEOSAT7MTSAT-1R×
GOES-W×
GOES-E×
METEOSAT

Under the formation of CCSR, HyARC, CAOS & CEReS (Virtual
Labo.; VL), our team archive & publish “global” geostational satellite
dataset.
Not only IR, WV, VIS mid-IR channels dataset is target to archive &releasing (http://www.cr.chiba-u.jp/~4vl/)

Operation Geo-Sat since 1998
Japan
(JMA)
China
(CMA)
EU
(EUMET
SAT)

USA(NASA)

Inter-comparison of MTSAT-1R and FY-2C
TBB FY2-C HighTBB FY2-C High
×

×

×

×

IR1

IR2

IR3

×
×
FY2-C MTSAT-1R

IR1

IR2

IR3

2007.8.5

935

Yamamoto et al., 2007

Comparison with TRMM VIRSTBB FY2-C High

Yamamoto et al., 2007

×
FY2-C

TBB MTSAT-1R High×
MTSAT-1R

36

GSICS(Global Space-based Inter-Calibration System)●

Mission–●Assure high-quality, inter-calibrated
measurements from the international
constellation of operational satellites tosupport the GEOSS goal of increasing
the accuracy and interoperability ofenvironmental products andapplications for societal benefit.

Goals
–

The primary goal of GSICS is toimprove the use of space-based global
observations for weather, climate andenvironmental applications throughoperational inter-calibration of thespace component of the WMO World http://www.star.nesdis.noaa.gov/
Weather Watch (WWW) Global
smcd/spb/calibration/icvs/GSICS/
Observing System (GOS) and Global
Earth Observing System of Systems(GEOSS).

(Hopefully) Useful Website●

JMA MTSAT: http://www.jma.go.jp/en/gms/●

GOES: http://www.goes.noaa.gov/●

EUMETSAT: http://www.eumetsat.int/●

CEReS VL website (most of them; in Japanese)– http://www.cr.chiba-u.jp/~4vl/wiki/wiki.cgi–

MTSAT ftp: ftp://mtsat-1r.cr.chiba-u.ac.jp/grid-MTSAT-1.01/–

FY-2X ftp: ftp://fy.cr.chiba-u.ac.jp/disk1/grided/–

GOES-E, W ftp: ftp://goes.cr.chiba-u.ac.jp/goes-e (or -w) /grid-GOES-1.0/●

MTSAT at Kochi University: http://weather.is.kochi-u.ac.jp/●

Digital Typhoon: http://agora.ex.nii.ac.jp/digital-typhoon/●

ISCCP: http://isccp.giss.nasa.gov/●

GSICS: http://www.star.nesdis.noaa.gov/smcd/spb/calibration/icvs/GSICS/

References●

Fujinami and Yasunari, JMSJ, 79, 1207-1227, 2001.

●

Kato, 2006, Graduate Thesis, Chiba University●

Kato, 2008, Master Thesis, Chiba University●

Kurosaki and Kimura, JMSJ, 80, 1339-1355, 2002.

●

Ohsawa et al., JMSJ, 79, 333-352, 2001.

●

Kondo et al., MWR, 134, 1581-1599, 2006.

●●

Kidder and Haar, Satellite Meteorology, Academic Press, 466p,
1995.
Sato et al., JGR, 112, D24109, doi:10.1029/2006JD008129, 2007.

Tropical Rainfall Measuring Mission(TRMM)

Kenji Nakamura
Hydrospheric Atmospheric Research Center
Nagoya University
The Eighteenth IHP Training Course
Nov. 11 (Tue) 2008

図５(a)Chou et al. JGR 1997

図５（ｂ） 顕熱フラックスの分布

Chou et al. JGR 1997

Meridional circulation of the atmosphere (Peixoto and Oort, 1991)

Meridional circulation of water vapor (Peixoto and Oort, 1991)

図８ 降水の緯度分布

図6 世界の降水の気候値(Adler et al., 2003)

Tropical Rainfall Measuring Mission: TRMM
Solar paddle

High-gain antenna

TMI

VIRS
LIS
PR

Observation of tropicalrainfall (Driving engineof global atmosphere)
US-Japan joint mission(Japan: PR, Launch,
US: Bus, 4 sensors,operation)

CERES

Launched in Nov., 1997.
Still under operation
Orbit
Altitude
Inclination

Circular（Non-Sun Synchronous）
350km (402.5km since Aug. 2001) (±1.25km)
35 deg.

SensorPrecipitation Radar (PR)TRMM Microwave Imager (TMI)
Visible and Infrared Scanner (VIRS)First space-borne
precipitation radardeveloped by CRL and
NASDA

Clouds and the Earth’s Radiation Energy System (CERES)

Lightning (LIS)COMMUNICATIONSRESEARCHLABORATORYTRMM Precipitation Radar

２m

DIV/COMB

Radar type
Pulse radar
Antenna type128-elem. WG slot array
Beam scanning
Active phased array
Frequency
13.796, 13.802 GHz
Polarization
Horizontal
TX/RX pulse width 1.57 / 1.67sec
RX band width
0.6 MHz
Pulse rep. freq.
2776 Hz
Data rate
93.5 kbps
Mass
460 kgDesigned Life time
3 years
Sensitivity< 0.5mm/h
Horizontal resolution 4.3 km (nadir)
Range resolution
250 m# of indpdt samples
64 (fading noise < 0.7 dB)
Swath width
215km
Observable range
Surface to 15km

3-D Observation of a Typhoon by TRMM
Aug. 2, 2000, 20:49-20:53 (Japanese local time)Rain intensity at H=2 km

Vertical cross section through the eye and
3D structure

PR realized observation of 3D structure of rain overocean where few observations had been available.

(IPCC3)

(IPCC3)

Precipitation over land increased by about 2 % in the 20 th century, but with large variations.

Global land precipitation variation
120
年
降 110
水
量
平
年 100
比

全球

（
％ 90
）
80
1880

平年値：1971-2000年統計
1900

The 3rd IPCC Report

1920

1940
年

1960

1980

2000

In Japan, about 6 % decrease in precipitation with larger interannual variations.

Precipitation variation in Japan
年 130
降 120
水
量 110
平
100
年
比 90
（
％ 80

）

70
1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010
年

JMA Report

1898年以降の国内51地点における年降水量平年比の平均。平年値は1971～2000年統計値。実線は5年移動平均。

(Adler et al., 2007)

(Adler et al., 2007)

(Adler et al., 2008)

Precipitation measurement with DPR
Detectable range of KaPR (35 GHz)

Matched beam of
KuPR and KaPR

( cannot measure heavy rainfall )ICEHeightDetectable range of KuPR (14GHz)( cannot measure light rain or snowfall)Sensitive observationby the KaPR

SNOW
Snowfallmeasurement in
the frigid zones

MELTING
LAYAR

Discrimination ofsnow and rainusing differential
attenuation

KuPR
KaPR

RAIN

Accurate rainfall
estimation using
differential attenuation(DSD parameter estimation)

Accurate rainfall measurement in
the tropics and the temperate zones

Radar reflectivity

(Maruyama and Fujiyoshi, JAS, 2005)

Global Distribution of the Mean Storm Height
Measured by the TRMM Precipitation Radar
July 1998

January 1999

図１０

図１１

(by Endo and Koike)7.5 km2 km

Latent heating from TRMM PR for 1998-2000.
(Takayabu et al.)The Concept of
Global Precipitation Measurement (GPM)

Core Satellite

Constellation
Satellites• Dual-frequency Precipitation
Radar (DPR)• Microwave Radiometer• Microwave Radiometers installed oneach country’s satelliteFrequent precipitation
measurement

High-sensitivity precipitation
measurement
Calibration for constellation
radiometers

Expected Partners:

NASDA (Japan) :NASA, NOAA (US),ESA (EU), NASDA,
China, Korea, others

DRP, H-IIA Launcher

NASA (US) :Spacecraft, MWR

Blue: Inclination ~65º (GPM core)
Green: Inclination ~35º (TRMM)

3-hourly
globalrainfall map

2 satellites

8 satellites

Time and space Interval of the DPR database
1-day orbit

Color: orbit num

3-days orbit

Color: IFOV pixel num
0.50.5deg

100 pixels

One-day orbit observations ofDPR (upper) and GMI (lower)
The orbit data was calculated by Y.Iida (Osaka Prefecture Univ)

100 pixels

DPR dynamic database will be renewedin 15 deg  5 deg grids every 3-10 days

Design of the GPM Core Satellite
and the DPR
GMI+x
FlightDirection

KaPR
KuPR
KaPR Additionalradiation panel

KaPR(Spacecraft design by NASA/GSFC)

Wave-guideconnectors

KuPR

Basic design of KuPR and KaPR isthe almost same as TRMM PR.

Concept of precipitation measurementby the GPM core satellite
Dual-frequency precipitation
radar (DPR) consists of
Ku-band (14GHz) radar : KuPR
andKa-band (35GHz) radar : KaPR

Flightdirection

DPR

GMI

Range resolution= 250mKuPR (13.6 GHz)
swath width=245 km

KaPR (35.5 GHz)
swath width=100 km

5km

Microwave radiometerswath width =800km

GPM/DPR Project Schedule
Calender Year
Japanese
Fiscal Year

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
FY14 FY15 FY16 FY17 FY18 FY19 FY20 FY21 FY22 FY23 FY24 FY25 FY26 FY27 FY28

(April-March)

Milestone
DPR

PRR

PDR

DRR

Conceptual Design

Preliminary
Design

BBM

CDR

DCR

PQR/PSR

LaunchOCR

Sustaining
DesignCritical Design

EM
STM

KuPR

PFM
BBM

EM (NICT)

Install & Test

STM

KaPR

OpTraining
Init. Op.

PFM
Ground System
Algorithm
Spacecraft Bus(NASA)

Conceptual Design
Conceptual Design

Prototype Development

SDR
Conceptual Design

Integ.
& MST

System Design Fab. & Test

PDR
Preliminary Design

Fabrication& Assembly

Operation

Improvement and Validation

Development

PQR/PSR

CDR

CriticalDesignTest
Op.

Install & Test

PRR : Project Readiness Review
DRR : Development Readiness Review
PDR : Preliminary Design Review
CDR : Critical Design Review

PQR : Post Qualification-test Review
PSR : Pre-Shipment Review
DCR : Development Completion Review
OCR : Operation-phase Completion

L8. Space borne Precipitation Radars- Rainfall profiling by space borne K-band radars- Current and future satellite missions carrying precipitation radars

Toshio Iguchi
Applied Electromagnetic Research Center,
National Institute of Information and Communications Technology
The Eighteenth International Hydrological Program (IHP) Training Course
Satellite Remote Sensing of Atmospheric Constituents
3-15 November 2008
Nagoya, Japan

Remote sensing of rain by radar• Radar emits a known pulse of radio waves andmeasures its echoes from objects or targets.
• The time for the pulse to travel to the target
gives the distance to the target.
• The direction of the radio waves gives the
direction of the target.
• The echo power depends on the size andnumber of the targets.

RADAR: RAdio Detection And Ranging
AircraftClouds

Reflected wave

Transmitted wave

Rain
Reflected waveRadar

3-D Observation of a Typhoon by the PR
Aug. 2, 2000, 20:49-20:53 (Japanese local time)Rain intensity at H=2 km

Vertical cross section through the eye and
3D structure

PR realized observation of 3D structure of rain over ocean where few
observations had been available.

Monthly Rain Distributions estimated from the TRMM
PR data in 1998 (El Nino year) and 1999

Strom Top Height Distribution
measured with the TRMM Precipitation Radar

Radar Equation

Concept of TRMM Rain Observation
FlightSpeed:
km/secc
飛行 速
度 : 77.3. 3 km/se

PR: 降 雨 レ ー ダPR:
TM I:Precipitation
TRM M マ イRadar
ク ロ 波 観測装 置
TMI: TRMM Microwave Imager
VI RS: 可 視 赤 外 観 測 装 置
VIRS: Visible/IR Scanner
PR
TM I
VI RS

2 50 m

7 60 km
2 15 km

7 20 km

6 ～ 5 0km

4 km
2 km

Factors that affect the Rainestimates from space-borne radar• Type of precipitation– rain, snow, groupel, hail, etc.

• Drop size distribution (DSD)• Flatness (non-sphericity) of rain drops• Fall speed of rain drops– need this quantity to convert reflectivity into rain rate

• Influence from matters other than precipitation– Clouds, water vapor, other gasses• Non-uniform distribution of precipitation• Variation of surface radar cross sections– Surface reference method

Peculiarities of satellite-borne radar
Differences from ground-based radar• Hardware constraints– size, mass, power consumption• use of short waves -> attenuation• sensitivity– reliability• Observation geometry– distance, angle• sensitivity, resolution– surface behind rain
• surface clutter– moving platform (unless from a geostationary
satellite)• difficulty in Doppler measurement

Footprint size and wavelength• Use of relatively high frequency (short wave) to realize
a good horizontal resolution.
– antenna beam width ~ c1l/D (wavelength/diameter)• l: wavelength of the electromagnetic wave
• D: antenna diameter• c1: a constant that depends on the antenna illumination (~1.2)– footprint size ~ c1rl/D (r: range to surface)– D < 2~3 m unless the antenna is developed on orbit– r > ~300 km.
– -> use a small l to make the footprint size (c1rl/D) small.
– to realize a 5 km footprint with a 2 m antenna from a
400km orbit, l ~ 5*2/(1.2*400) m = 2.08 cm (= 14.4GHz)

Issues associated with short waves• Attenuation– rain, snow, water vapor, cloud liquid water (liquidcloud droplets), and oxygen molecules– Correction methods:• Hitschfeld-Bordan method, Surface reference method• Non-Rayleigh scattering effect– scattering cross section does not change
proportionally to D6– Drop size distribution modelAttenuation by rainAttenuation by H2O and O2

Attenuation by O2

Attenuation by H2O

Surface Reference Technique

HeightDecrease of the apparent surface echo
( Ds 0 ) under rain is interpreted as the pathintegrated attenuation (PIA) due to rain.

Attenuated
Echo profile
with Rain

Echo without
Rain

Echo after attenuation

correction

Ds ’0Rain Echo

Surfaceclutter
Ds 0

Surface Echo

Ds 0

Use this PIA to correct for
the attenuation of rain echonear the surface.
In practice, the difference
between the PIA to thesurface and the PIA to theclutter-free bottom must be
taken into account.

ZDrop Size Distribution (DSD)• Both k-Ze and R-Ze relations
depend on DSD.
• Hitschfeld-Bordan’s solution
assumes a k-Ze relation.
• When the SRT is not applicable,
the initial DSD determines theattenuation correction and theZe-to-R conversion.
• When the SRT is applicable, acan be adjusted to match the HB estimate of PIA to the SRT PIA.
This in effect corresponds toadjusting the initial DSD.

Hitschfeld-Bordan solution

DSD variation in Indian rain
Averaged DSDs in NE monsoon

Averaged DSDs in SW monsoon
4

2
1

-1

-1
2
3
Diameter (mm)4

5

R = 30 mm/h

1
01

R = 10 mm/h

2

0

0

R = 1 mm/h
R = 3 mm/h

3log10 of N(D)3

log10 of N(D)4

R = 1 mm/h
R = 3 mm/h
R = 10 mm/h
R = 30 mm/h

0

1

2

3

4

5

Diameter (mm)Averaged Dropsize Distribution during South-West (SW) and North-East (NE) monsoon seasons in Gadanki, south
India in 1997 and 1999. SW and NE seasons are between May and October, and between November andDecember, respectively.
DSDs within +/- 1 dB centered at the rain rate specified are averaged.
T. Kozu, K. K. Reddy and A.R. Jain
Oct. 20, 2000

Z-R relations
in SW and NE Indian monsoon seasons
60
50South-west
monsoonSeptember
1997(Sep.97)

60
50
40

30

dBZ

dBZ

40
Dec.'97 Z- R

30
20

20
10
0

10
dBR

20

y = 1.5602x + 19.623
R2 = 0.8539

10

y = 1.3385x + 25.952
R2 = 0.9165

0
-10

0
-10

30

Seasonal Variation of a and b in Z-R
Transition
60
SW

a_97/10b_97*10a_99/10b_99*1080

a and bNorth-east
monsoon(Dec.97)
December
1997

40

NE

20
0

5

6

7

8

9

Month

10

11

12

0

10
dBR

20

30

SW Z = 405R 1.29
NE Z = 144R 1.38
Strat/Conv separation:
Not significant

Off-nadir observations (1/2)• Echo power consists of reflectivity
distribution weighted by the antenna
gain pattern and the pulse shape.
• Since the beam width near surface ismuch larger than the range
resolution, the effective verticalresolution degrades with off-nadir
angle.

(From Meneghini and Kozu)

Off-nadir observations (2/2)• Surface echoes are generally muchstronger than rain echoes.
• At off-nadir, surface echoes may
contaminate rain echo near thesurface. (Main-lobe problem)(From Meneghini and Kozu)

Difference between radar andradiometer retrieval of rain
• Radar– well defined range information• Height information is very reliable.

– Z-R relation scatters more than Tb-R relation• Echo power is more sensitive to DSD variation than Tb.

– Number of wavelengths is very limited in practice.

• Radiometer– Needs to assume a vertical precipitation profile.
• e. g., freezing heightComparison of different satellite rainfall
estimation algorithms
Mean DJF Rainfall (1987 – 1996)

Infrared

Microwave Radiometer（50GHz）

Microwave Radiometer(Multi-channel)(W. Berg, et al.)Comparison between TMI and
PR rainfall estimates(Bias Adjusted TRMM Retrievals)(W. Berg, et al.)Time series of monthly mean rainfall anomalies
over Tropical ocean－Comparison among different passive microwave algorithmsー(W. Berg, et al.)Time series of monthly mean rainfall
anomalies over Tropical ocean(TRMM Ocean Retrievals)

TMI

PR

(W. Berg, et al.)

Passive Microwave Retrievals
Column integrated water vs rainfall rate

Freezing Level

Tb's in the low frequency channels of a microwave radiometerare proportional to the column integrated rain water content.

0C

0C

Rain Profile

Surface Rainfall

0C

Surface Rainfall

0C

Surface Rainfall

Surface Rainfall

PR Bright-Band Heightand TMI Freezing HeightTMI Freezing Height – PR Bright Band Height(W. Berg, et al.)PR and TMI Regional Validation

TMI V6, PR V5(W. Berg, et al.)Current and future satellite missions
carrying a precipitation or cloud radar• Tropical Rainfall Measuring Mission (TRMM) / PR
– November 1997 – present– Single frequency (13.8 GHz),– 250 km swath, >18 dBZ, 250 m v. res.

• Global Precipitation Measurement (GPM) / DPR– 2013 (launch)– Dual-frequency (13.6 GHz, 35.5GHz)– 250 km swath, >12dBZ (Ka, 125 km swath, 500 m vert. res)• CloudSat/CPR– April 2006 – present– 94GHz, nadir only, > -30dBZ, 500 m vertical res.

• EarthCARE/CPR– 2013 (launch)– 94GHz, nadir only, > -35dBZ, 500 m vertical res.
– DopplerTropical Rainfall Measuring Mission: TRMM
Solar paddle

High-gain antenna

TMI

VIRS
LIS
PR

Observation of tropicalrainfall (Driving engine ofglobal atmosphere)
US-Japan joint mission (Japan:
PR, Launch,
US: Bus, 4 sensors,operation)

CERES

Launched in Nov., 1997.
Still under operation
Orbit
Altitude
Inclination

Circular（Non-Sun Synchronous）
350km (402.5km since Aug. 2001) (±1.25km)
35 deg.

SensorPrecipitation Radar (PR)TRMM Microwave Imager (TMI)
Visible and Infrared Scanner (VIRS)First space-borne
precipitation radardeveloped by CRL and
NASDA

Clouds and the Earth’s Radiation Energy System (CERES)

Lightning (LIS)COMMUNICATIONSRESEARCHLABORATORY

3-D Observation of a Typhoon by the PR
Aug. 2, 2000, 20:49-20:53 (Japanese local time)Rain intensity at H=2 km

Vertical cross section through the eye and
3D structure

PR realized observation of 3D structure of rain over ocean where few
observations had been available.

What’s Next to TRMM? GPM
A Mission to:•Measure a broader spectrum of precipitation(e.g. light rain, snow)•Provide measurements in the tropics and midlatitudes (e.g. global)•Provide global precipitation products every 3
hours with 90% accuracy•Further reduce uncertainty in precipitation
microphysics and rainfall-radar reflectivity
measurements•Provide global precipitation measurements at
temporal scales needed by weather, climate, andhydrological models•Enable new societal applications in weather
forecasting, flood prediction, freshwater resource
management, public communications, andeducation

GPM Era Global Coverage

The Mission is Global Precipitation
Measurement (GPM)Scientific and Social Significance of GPM
Precision brought by DPR– High sensitivity to detect weak rain
and snow– Accurate estimation of rainfall rate
– Separation of snow from rain
– Progress in cloud physics
Global rain map in every 3 hours by GPM– Climate change assessment
monitor variations in rainfall and rain areas
associated with climate changes and global
warming– Improvement in weather forecasts
Quasi-real-time assimilation of data in numericalprediction models,
Improved flood prediction– Water resource management
river, dam, agricultural water, etc.

– Agricultural production forecastingGPM Reference Concept
OBJECTIVE: Understand the
Horizontal and Vertical
Structure of Rainfall and Its
Microphysical Element.
Provide Training for
Constellation Radiometers.
Core Satellite• Dual Frequency Radar• Multi-frequency Radiometer• H2-A Launch• TRMM-like Spacecraft• Non-Sun Synchronous Orbit• ~70 deg. Inclination• ~400 - 500 km Altitude• ~5 km Horizontal Resolution• 250 m Vertical Resolution
Precipitation Validation Sites• Global Ground Based Rain
Measurement

OBJECTIVE: Provide EnoughSampling to Reduce
Uncertainty in Short-term
Rainfall Accumulations.
Extend Scientific andSocietal Applications.
Constellation Satellites• Small Satellites with
Microwave Radiometers• Aggregate Revisit Time,
3 Hour goal
• Sun-Synchronous Polar
Orbits• ~600 km Altitude

Global Precipitation Processing Center• Capable of Producing Global Precip Data
Products as Defined by GPM Partners

GPM Core Satellite
Core

Microwave
radiometer

Ku-band precip. radar

Ka-band precip. radar

Frequency
Sensitivity

Ku-band (13.6 GHz), Ka-band (35.5 GHz)
Ku-band --- same as TRMM PR (18 dBZ, Dz=250 m)
Ka-band --- same as Ku-band (18 dBZ, Dz=250 m)--- 6 dB better than Ku-band (12 dBZ, Dz=500 m)
Swath width 245 km (Ku), >100 km (Ka)
Footprint
5 km

Frequency
Swath width
Footprint

10 GHz --- 85 GHz (5 bands, 9 channels) --- similar to TMIApproximately 900 km (Conical scan)
40 x 60 km (10 GHz) --- 5 x 8 km (85 GHz)

Dual Frequency Precipitation Radar
14GHz
radar
beam

Detection limit in 35GHz channel

35GHz
radar
beam

Detection limit in 14GHz channel
Higher sensitivity at higher frequency

Ice
HeightSnow

Discrimination between snow and rain
by attenuation difference

Melting layer
Rain

14GHz
３５GHz

Accurate rain estimation basedon attenuation difference
Radar Reflectivity Factor

Measure 3-D structure of rain as TRMM, but with better sensitivity
Accumulate climatological precipitation data continuously since TRMM
Improve estimation accuracy with dual-frequency radar
Identification of hydrometer type
Estimation of DSD parameters

DPR Specifications (Tentative)
Item

13.6-GHz radar (Ku-PR)

35.5-GHz radar (Ka-PR)

Antenna TypeBeam Number
Swath Width
Pulse Width
Range Resolution
Beam Width
Horizontal Resolution
PRF
Peak Power
Sensitivity
Data Rate
Mass
Power Consumption
Dimensions

Active Phased Array
Active Phased Array
49
20+19+x
245 km100 km1.67 micro sec. (x2)
1.67/3.34 micro sec. (x2)
250 m
250 m / 500 m
0.7 deg.
0.7 deg.
5 km
5 km
VPRF (4000 Hz  250 Hz) VPRF (4500 Hz  250 Hz)
1000 W
144 W [current]
17 dBZ (0.4 mm/hr)
12 dBZ (0.2 mm/hr) [target]
95 kbps
95 kbps
370 kg
290 kg334 W [max 352 W]
306 W [max 331 W]
2.4×2.4×0.6 m
1.3 ×1.0 ×0.7 m

CloudSat CPR System Characteristics
•
•
•
•
•
•
•

Nominal Frequency 94 GHz
Pulse Width 3.3 µsec
Minimum Detectable Z* < -29 dBZ PRF 4300 Hz
Data Window 0-25 km
Antenna Size 1.85 m
Dynamic Range 70 dB
Integration Time 0.16 sec
Nadir Angle (since 15 Aug 2006***) 0.16°
Vertical Resolution 500 m
Cross-track Resolution 1.4 km
Along-track Resolution** 1.7 km Data Rate 20 kbps•

*Equivalent radar reflectivity that gives a mean power equal to the standard deviation after
integration and noise subtraction. Atmospheric attenuation is not included.
**The along-track resolution is based on averaging the instantaneous footprint over the
integration time. Based on purely geometric arguments, the along-track resolution would beapproximately 2.5 km. However, a more rigorous convolution calculation gives an along-track
resolution of 1.7 km, as shown in the table.
***Nadir angles were changed from approx. 1.71° to 0.0° on 7 July 2006 and from 0.0° to
0.16° on 15 August 2006••

What is EarthCARE ?
EarthCARE (Earth Clouds, Aerosols and Radiation
Explore)•Satellite mission selected as the sixth Earth Explorer Mission in 2004 of
ESA•European Space Agency (ESA) and Japanese (JAXA, NICT) collaboration•Objective: global clouds and aerosol vertical observation for global
radiation budget•Launch: 2013
•Instrument:
Cloud Profiling Radar (CPR)
Atmospheric Lidar (ATLID)
Multi-spectral Imager (MSI)
Broad Band Radiometer (BBR)

47

MSI (ESA)
Visible & infrared imager
150 km across track
Object: Clouds, Aerosol
Horizontal structure

Orbit• Sun-synchronous• Inclination 97 degrees• Altitude: 450 km (TBD)• Local time: 13:30
descending node
• Period: 94 min• Mission life: 3 years

CPR (Japan)
W-band Doppler radar
Nadir

BBR (ESA)

Object: Cloud, Rain

Broad band radiometer

Vertical structure+-50 & 0 degree alongtrack
Object: Radiation flux (longand short wave)

ATLID (ESA)• Revisit period: 10 - 30 days
(TBD)
Satellite• Mass: 1300 kg• Power: 1100 W•Data rate: up to 1500
kbit/sec

UV lidar4 degree forward(TBD)
Object: Cloud, Aerosol
Vertical structure

48

CPR structure and function
Rx2
EIK2
Tx2

Main reflector

QOF

Tx1

Deployment
mechanism

Rx1
EIK1

Quasi-optical feeder

AntennaTransmitter

EIK

QOF

LNA
RecieverUp coverter

Platform for Tx/Rx

Pulse
modulaor

Standard
Oscilator

Down
converter

Data processor

49

CPR specifications
Tx frequency

94.05 GHzTx peak power

1.5 kW (EOL)

Pulse width

3.3μs

Polarization

Circular

Antenna diameter
Beam footprint size
Beam direction
Minimum sensitivity
Data sampling

2.5 m
700 m
Nadir
-35 dBZ (10km average)
100 m (Vertical)
500 m (Horizontal)
Pulse pair

Doppler measurement50Difference from CloudSat CPR•Higher sensitivity (about 10 dB) from bigger antenna andlower orbit
minimum detection: -35dBZ at TOA (10km integration)•Doppler function (pulse–pair method)
vertical velocity measurement
accuracy: 1 m/ s (10km integration -19dBZ echo)•Variable PRF and height range•Co-registration with Lidar and other sensors•Circular polarization•Less ground clutter effect•Longer life transmit tube (EIK)

51

Error in Doppler measurement (10km integration error including antenna, satellite, etc.)-35 dBZ (blue)

0.86m/s(6100Hz, -19dBZ)

every 1 dB step(<= -10 dBZ)

0.67m/s(6600Hz, -19dBZ)-30 dBZ (blue)

0.55m/s
0.60m/s(7200Hz, -19dBZ)

0.55m/s(12800Hz, -19dBZ)-25 dBZ (blue)-20 dBZ (blue)-19 dBZ (S/N 2.2 dB) (red)-15 dBZ (blue)-10 dBZ (blue)
0 dBZ (purple)
52

Spaceborne Atmospheric Radars
TRMM/PR – NICT/JAXA
Ku, Scanning , Tropical Rain

GPM/DPR – NICT/JAXA
Ku/Ka, Scanning, Precipitation

ACE Radar (one concept)
W/Ka, Scanning, Doppler

1997-Today
CloudSat/CPR – JPL/NASAW, -30dBZ , Clouds
EarthCARE/CPR – NICT/JAXA
W, Doppler, Clouds

2006 -Today
NASA/JAXA worshop on ACE Mission –Lihue July 29-31 2008

Global Precipitation Mapping

Tomoo Ushio
Osaka University, Osaka, Japan

INTRODUCTION

Scientific and Social Significance of the global
precipitation map

Global rain map in daily to hourly scale
Climate change assessment
Monitor variations in rainfall and rain areas
associated with climate changes and global
warmingImprovement in weather forecasts
Data assimilation in numerical prediction systems



Flood prediction
Water resource management
River, dam, agricultural water, etc.



Other applications
Agriculture, etc.

Precipitation measurement from space?

Remote Sensing from space using electromagnetic waves
Emission, absorption andscatteringby rain drops, snow flakes etc

Infrared radiometer

Cloud particle
Snow and ice
Mixture ofsnow andprecipitation
Melting layer
Precipitation

Sea Surface Wind

Cloud top temp.
Scattering

Microwave radiometer

Precipitaion radar on satellite
Scattering from rain drops
Radiation

0℃Height

Precipitation characteristics observed by
the space borne sensors
(a)

(b)

(c)

(d)

(a) Precipitation radar
Back scattering from rain drops
High accuracy
Narrow swath width(b)Infrared radiometer：
Cloud top information
Not related to surface precipitation rates(c)Microwave radiometer(19GHz)：(d)Microwave radiometer(85GHz):
Directly measures the emission fromprecipitation particularly in low
frequencies

Excellent!!

Global Precipitation Product

TRMM
TMI

Aqua
AMSR-EADEOS-II
AMSR

DMSP
SSM/I

Microwave radiometer algorithm

Product from each sensor
Geo. Satellite
Infrared radiomter
Cloud motion vector

Mix

TRMM/TMI, Aqua/AMSR-E,
ADEOS-II/AMSR, DMSP/SSMI(F13, 14, 15)による1時間のデータ

Composite
product
0.5degree

30 min.
6 hour1 day
1 monthComposite algorithm of
IR and MWR
0.1degree・30min6 hourly MWR combined map
TMI

AMSR & AMSR-E

Combined
6 hourly

SSM/I （F13, F14, F15）

Global Precipitation product

TRMM
TMI

Aqua
AMSR-EADEOS-II
AMSR

DMSP
SSM/I

Microwave radiometer algorithm

Product from each sensor
Geo. Satellite
Infrared radiomter
Cloud motion vector

Mix

TRMM/TMI, Aqua/AMSR-E,
ADEOS-II/AMSR, DMSP/SSMI(F13, 14, 15)による1時間のデータ

Composite
product
0.1degree

1 hour
6 hour1 day
1 monthComposite algorithm of
IR and MWR
0.1degree・30min

How can we get a global precipitation map
with temporal resolution of 3 hours or less?


Infrared radiometers (IR)can provide information on cloud top layers (not precipitation)
Can ensure a global coverage with high temporal resolution (> 30 min)due to the geo-synchronous orbit (GEO)

Microwave radiometers (MW)Can detect cloud structure and precipitation with high spatial resolution
The major draw back is temporal sampling due low earth orbit satellite
(LEO)



The LEO-MW and GEO-IR radiometry are quite complementary formonitoring the highly variable parameters like precipitation.

Global High Time/Space Resolution Precipitation Map
CMORPH (CPC Morphing) – NOAAPERSIANN (Precipitation Estimation from RemotelySensed Information using Artificial Neural Networks )- University of California Irvine
TRMM 3B42 – GSFC, NASA
TRMM 3B42rt – GSFC, NASA
NRL Blended - Naval Research Laboratory
NESDIS Hydro Estimator (STAR) - NOAA
Eumetsat Multi-Sensor Precipitation Estimates(MPE)High Resolution Precipitation Products

PERSIANN 6-hourly/0.25° real-time analyses

TRMM 3-hourly/0.25° real-time analyses

High Resolution Precipitation Products

NRL (Turk) 3-hourly/0.25° real-time analyses

CMORPH 30-minute/8 km real-time analyses

CMORPH -A High Time-Space Resolution Global
Precipitation Analysis Using Passive Microwave and Infrared
Data
http://www.cpc.ncep.noaa.gov/products/janowiak/cmorph.html
Joyce et al., 2004, J. Hydrometeorology

 Team: Bob Joyce, John Janowiak, Pingping Xie(Climate Prediction Center, NOAA)
 Concept:
Take maximum advantage of accuracy of microwave
estimates and coverage of IR
Don’t use IR to estimate precipitation – all methods
developed so far have significant and difficult-toquantify errors, particularly on fine scales
Use IR to estimate storm motion instead – errors are
smaller and easier to understandAlgorithm Output Information
 Spatial Resolution: ~ 8 km (at equator) Domain: 60N-60S (global longitude) Update Frequency: 30 minutes
 Data Latency (~ 15 hours; 2.5 hrs for “QMORPH”)
 Source of Real-Time Data: NESDIS/NCEP
 Source of Archive Data: NESDIS/NCEP Capability of Producing Retrospective Data:

beg’g in 1-2 months – back to ~1998

Global Precipitation Climatology Project(GPCP)(part of WCRP/GEWEX)
NASA, NOAA, DWD, Universities, EUMETSAT, JMA

GPCP Global Precipitation Products• Monthly, 2.5º merged analysis[1979-present](Adler et al., 2003,submitted to J. Hydromet.;
Huffman et al., 1997, BAMS)• Pentad, 2.5 º merged analysis [1979-present] (Xieet al., 2003--accepted J. Clim.)• Daily, 1º merged analysis [1997-present](Huffman et al. 2001, J. Hydromet.)[products are integrated, i,e. they add up]
produced ~ 3 months after observation time
see www.precip.gsfc.nasa.gov

23-Year Climatology
GPCP Monthly Product•SSM/I, geo-IR, TOVS,
OLR, gauges•Stepwise bias removal•Blending using inverse
error weights• Final error fields•WCRP/GEWEX• Adler et al. (2003) J.
Hydromet. (submitted)
precip.gsfc.nasa.gov
Global total = 2.6 mm/d

(Ocean [ 2.8 mm/d ]Land[ 2.1 mm/d ])TRMM Multi-Satellite Real-Time Precipitation Estimates

3B40RT
Combined
microwave (TRMM
and TRMM-calibrated
SSM/I)
3B41RT
Microwavecalibrated geo-IR
estimate(Geo-IR from
NOAA/CPC)

3B42RT
Merged microwave
and geo-IR
estimates
Adler/Huffman

Multi-satellite Precipitation
Analysis (MPA)
George Huffman, Bob Adler, Dave Bolvin, Eric NelkinNASA/GSFC

 Algorithm

ProcessThe “real” MPA is
the post-real-time
Version 6 3B42
The current realtime 3B42RT thatruns 10 hr after
real time is a firstlook
We plan to replace
the 10-hr run with a4-hr “early” and 7hr “final” to bettersupport operations

Instantaneous
SSM/I
TRMM
AMSR
AMSU

Calibrate High-Quality(HQ) Estimates to“Best”

30-day HQ coefficients

Merge HQ Estimates
3-hourly merged HQ
Match IR and HQ,generate coeffs

3-hourly
IR Tb

30-day IR coefficients
Apply IR coefficients
Hourly HQ-calib IR
precipMerge IR, merged HQ
estimates
3-hourly multi-satellite(MS)

MonthlygaugesCompute monthly
satellite-gauge
combination (SG)Monthly SG
Rescale 3-hourly MS tomonthly SG
Rescaled 3-hourly MS

Algorithm Output Information
 Spatial Resolution

0.25°x0.25°
 Spatial Coverage

50°N-S (60° possible) Update Frequency

3-hr (RT), monthly (V.6)
 Data Latency

10 hr (RT; plan move to 7 & 4),
2 weeks after month (V.6)

Theory / Schematic (cont.) Strengths and Weaknesses of Underlying

Assumptions








“All” MW are used
Aggregating MW to 3-hr maps introduces time errors at fine scaleIntercalibration of MW and cal. of IR to MW traceable; good, except bad if
the calibrator goes bad
Different calibrators are used for RT (TMI) and V.6 (TCI and gauge)
VAR scheme for IR assumes that instantaneously colder clouds rain more
VAR scheme for IR takes calibration from a month of data; stable, but stiff
HQ-VAR combination scheme is priority - HQ, else VAR; minimum
assumptions, but introduces boundaries

GLOBAL SATELLITE MAPPINGOF PRECIPITATION (GSMAP)Four pillars of the research

GEO
meteorologicalsatellite

Production of global precipitation maps

TRMM/TMI

Improvementof algorithm

Aqua/
AMSR-EADEOOS-II/
AMSR

DMSP/
SSM/I

Algorithm development
Observation data

IR dataCloud
motionvectoralgorithm

High temporal
resolution map

Precipitation
map
database

Feedback

Internet

Look-up table

Data
dissemination
Ground radar observation

Estimationofrain rate

COBRA data

Radiative transfer equation
Precipitation physical model development

Local
precipitation
map
TRMM/PR

Globalprecipitation
map byTRMM/PR
Observation by TRMM/PR

Database of
precipitation
structure

Rain type classification
Precipitation profile
DSD
Melting layer
Snow

Precipitation
profileGlobal Precipitation Product

TRMM
TMI

Aqua
AMSR-EADEOS-II
AMSR

DMSP
SSM/I

Microwave radiometer algorithm

Product from each sensor
Geo. Satellite
Infrared radiomter
Cloud motion vector

Mix

TRMM/TMI, Aqua/AMSR-E,
ADEOS-II/AMSR, DMSP/SSMI(F13, 14, 15)による1時間のデータ

Composite
product
0.5degree

30 min.
6 hour1 day
1 monthComposite algorithm of
IR and MWR
0.1degree・30minBasis of Rain Rate Retrieval by Microwave Radiometers

Radiation Transfer
Equation
Observation
Data
Verification
Algorithm

Rain rate
Retrieval

Precipitation Physical
Model
Look-up
Table

Rain rate
Rain type, Rain profile
Rain drop size distribution
Melting layer, Snow, e.t.c.

• Satellites observe the brightness temperature, integration of radiation andscattering power.
• The relation between rain rate and brightness temperature is tabulated byassuming precipitation physical model and calculating the radiativetransfer equation. The rain rates giving the nearest brightness
temperature values to the observed ones are considered to be the most
28appropriate estimation.

Flow Diagram of GSMaP MWR Algorithm
Satellite Data
• Brightness temperatures (e.g. 9 channels)• Latitude/longitude information
JMA Objective Analysis Data
LatitudeTemperature Profile
Brightness
GANAL
Longitude(0ºC height)
temperature
Land or Ocean
SSTFirst guess of rain rate (85GHz-Only)

Ocean

Rain / No rain

Land

Correction of LUT using estimated inhomogeneity
Estimation of rain
rate (all frequencies)

Iteration

Estimation by
Scattering Algorithm(37,85GHz)

Output : Surface rain rate

Look-up Table (LUT)
Radiative transfer equation
Brightness temperaturevsRain rate(for every frequency)
Precipitation Physical Model
Precipitation Profile
Bright-band ModelDSDHistory of the algorithm modification

Comparison of TRMM rain rates by using TRMM/PR,
TMI/GPROF, TMI/GSMaP algorithms (1998-2005)31

Approach of the GSMaP project We use the Aonashi Algorithm to retrieve rainfall

rate.
 The sensors for the analysis are TMI, AMSR-E,
AMSR, SSMI (F13, 14, 15).
Name

Data available

TRMM (TMI)

Jan. 1998 to Dec. 2005

Aqua (AMSR-E)

Jan. 2003 to Oct. 2005

ADEOS-II (AMSR)

Apr. 2003 to Oct. 2003

DMSP (SSMI:F13, F14, F15)

Sep. 2003, July. 2005 and several

Monthly precipitation accumulation from
DMSP/SSMI (F13, 14, 15) for Sep. 2003

F13

F15

F14

Integrated 6-hour microwave radiometer
precipitation map (GSMaP_MWR)
MWR(TMI+AMSR+AMSR-E+F13, 14, 15 SSM/I)

37

Comparison of monthly rain rates by groundbased rain gauge (GPCC) with GSMaP_MWR
z GPCC Monthly Precipitation(Monitoring) Product （ Rudolf
et al. 2005）
 ground-based rain gauge
 about 7000 rain gauges in theworld 1.0ﾟ ×1.0ﾟ monthly averagez Analysis method• Tropical Area （20S～20N）• There are at least 2 rain
gauges in the 1.0ﾟ ×1.0ﾟ
• 2003-2005

Regression line : y=1.20 x + 30.8 (mm/month) Correlation coefficient : 0.79
38

If we need a global precipitation
map with higher resolution, howshould we do?
 Solution????

Global Precipitation product

TRMM
TMI

Aqua
AMSR-EADEOS-II
AMSR

DMSP
SSM/I

Microwave radiometer algorithm

Product from each sensor
Geo. Satellite
Infrared radiomter
Cloud motion vector

Mix

TRMM/TMI, Aqua/AMSR-E,
ADEOS-II/AMSR, DMSP/SSMI(F13, 14, 15)による1時間のデータ

Composite
product
0.1degree

1 hour
6 hour1 day
1 monthComposite algorithm of
IR and MWR
0.1degree・30min

Approaches
 There are so many products to realize the

3 hours or less resolution.
 What is the GSMaP approach?

How do we combine the MWR and IR data?
Combination of the moving vector and Kalman filtering methodThe moving vector method was introduced by Joyce et al. [2004].
Joyce R., J. Janowiak, P. Arkin, and P. Xie, CMORPH: A method that produces global precipitation estimates from passive
microwave and ifrared data at high spatial and temporal resolution, J. Hydrometeorology, 487-503, 2004

Advantage
MWR based approach (not Tb but cloud motion)
 Fast processing time

Disadvantage

Not include the developing and decaying process of precipitation

New!!


Kalman filter approach
Refine precipitation rate on Kalman gain after propagating the rain pixel
The Kalman gain is determined from the database on the relationshipbetween the IR Tb and surface rain rate.

State and observation equation
used in Kalman filterx k +1 = x k + σ w （ State Equation ）yk = Hxk + G + σv （Observation Equation）
xk ：Rain rate at time kyk ：Infrared Tb
xk +1：Rain rate at time k+1
w ：System noisev ：Observation noise

σ w2
α= 2
σv

Kalman Filter
Predicted rain rate

IR Tb

y
Obs. Noise σ v

xRefinementxˆ = x + K ( y − x )

GSMaP

x̂

Prediction
System Noiseσwx ′ = xˆ
Predicted rain ratex′K=

α 2 + 4α − α2Production of high temporal (1 hr)/high spatial (0.1ﾟ×0.1ﾟ)resolution precipitation map (GSMaP)
Algorithm flow to predict the movement of raining areas by applying the cloud
motion vector of the past 1 hour estimated from the IR cloud image
Infrared (IR) Data
11.4 μm Geo IR
Present
11.4 μm Geo IR
1 hour before

1 hr Moving Vector

Microwave Radiometer (MWR) Data
Predicted GSMaP
1 hr MWR
Present

Kalman Filter

GSMaP Data
GSMaP
1 hour before

GSMaP
Present

Backward process

Rain area obtained from the radiometer

Swath of the sensor

Backward process
forward

２hr

３hr

１hr
０hr

Propagating rain pixels along withcloud motion vector every hour

Backward process
forward

２hr

３hr

１hr
０hr

Newly identified rain area

Revisted Path of the microwave sensor

Backward process
forward

２時

１時
０時

３時

Backward process
forward

２時

１時
３時

０時
２時
１時

backward

t+1

t+2

+

+

+

=

=

=

t

Combined global precipitation map-MW radiometer + cloud motion vector with Kalman filter(0.1ﾟ , 1 hour, 8-10 July 2005)
MVK: MWR(TMI+AMSR+AMSR-E+F13, 14, 15 SSM/I)+IR Cloud Motion Vector +Kalman Filter赤外・マイクロ波放射計 複合アルゴリズムによる
降水量（2005年7月9～10日）

57

赤外・マイクロ波放射計 複合アルゴリズムによる
降水量（台風200507号/BANYAN）

58

Correlation between radar and the GSMaP product asa function of the past microwave satellite overpass

With Kalman filter

Without Kalman filter
Moving vector only

VALIDATION OF THE
PRECIPITATION PRODUCTS

Hurricane Katrina – August 2005
TRMM 3B42, CMORPH, NRL, PERSIANN

These products are very impressive, but
how good are they really?
It is important to evaluate these new highresolution precipitation products tounderstand their errors and to identify apath to a consensus product that can be
combined with gauge observations andother appropriate information and used forscientific and social applications

Program for the Evaluation of High Resolution
Precipitation Products (PEHRPP)
A comprehensive hypothesis-based collaborative effort tounderstand the capabilities and characteristics of these new
HRPP (High Resolution Precipitation Products)
 Hypotheses:


HRPP errors can be characterized by comparing them toindependent observations from rain gauges and radars.
Errors of and differences between HRPP are meaningful, in that they
can be systematically related to precipitation characteristics and/or
algorithm methodology.
Improved HRPP can be derived by combining products or methods
based on the observed errors and differences.
HRPP spatial and temporal variability is realistic on scales
appropriate for scientific studies (e.g., hydrology).
Numerical weather prediction forecasts of precipitation can be used
to improve HRPP in some locations and times (e.g., high latitudes).


Sponsored by the International Precipitation Working Group(Working Group of CGMS) with broad voluntary participation

PEHRPP Consists of 4 Suites of Activities
Suite 1: Continental/Regional Comparisons

Large areas, long (continuous) time periods
Daily/0.25°x0.25° areas

Current/Proposed PEHRPP Suite 1 Validation Sites

Original:
Australian continent (Ebert), CONUS (Janowiak), UK/Europe (Kidd)
New:South America (Vila), S Africa (Pegram), Korea (Sohn), Taiwan (Jou),
Japan (Ushio), Ethiopia/Sub-Saharan Africa (Dinko), Guangdong(Liang)…

Validation
GSMaP_MVK

CMORPH(NOAA/CPC)

GSMaP_MWR

3B42RT(NASA)Example of validation of GSMaP_MVK
using Radar-AMeDAS (8 July 2005)

GSMaP_MVK

67

Radar-AMeDAS

Evaluation of various high resolution precipitation map
using Radar-AMeDAS rain map
Daily variation of correlation coefficient (0.25ﾟ×0.25ﾟ）) July, 2005
Correlation

1

GSMaP_MVK
GSMaP_MVK+

0.9

Correlation

CMORPH
0.8

3B42RT

0.7

CMORPH: NOAA/CPC
3B42RT: NASA/GSFC

0.6

0.5
7/2

7/3

7/4

7/5

7/6

7/7

7/8

7/9

7/10

7/11

Date

GSMaP_MVK shows high correlation with Radar-AMeDAS throughout the period.
GSMaP_MVK+, produced by adding NOAA AMSU rain rates to GSMaP_MVK, shows particularlyhigh correlation.

GPCP
Daily
(NASA/GSFC)

CMAP(NOAA/
CPC)

降水マップの比較(8-12 Sept. 2003)（左上）：CMAP, 2.5°格子（右上）：GPCP Daily, 1.0°格子
雨量計と衛星推定雨量（IR, SSM/I, TOVS）複合（左下）：GSMaP, 0.1°格子
衛星推定雨量(TMI, AMSR-E, SSM/I)
のIR補間

GSMaP

REAL TIME SYSTEM FOR
APPLICATION

Construction of System for Near-Real-Time
Global Rainfall Maps by GSMaP algorithms

JAXA/EORC has started to release global rainfall data in near real time(about four hours after observations) on the Internet using GSMaP
algorithms.

GSMaP NRT System in JAXA/EORC
Global Rainfall Map in Near Real Time by
JAXA/EORC
http://sharaku.eorc.jaxa.jp/GSMaP/Six hourly animation

Accumulated rainfall amountfrom Apr. 27 to May 5

Summary Needs for IR and MWR integration method

are described.
 Some of the high resolution products are

introduced.
 GSMaP_MVK product was introduced.

1

An Introduction to GrADS Software

The Grid Analysis and Display System (GrADS) is an interactive desktop tool to display earthscience data. The followings are the features of GrADS.
¶³
Advantages• Free software. Without any costs to introduce GrADS.
• Easy installing. GrADS is distributed for several operation systems; UNIX, Linux, Mac
OSX, Sun, and Windows. In addition, pre-compiled versions are available.
• Interactive handling. It is suitable to quick-view data and is enable to calculate somekinds of functions easily.
• Multiple file time series. It is easy to display a time series such as latitude-time section.
• Easy access to variable data formats. GrADS deals with regardless of many types (i.e.
1-, 2-, and 4-byte integer (signed and unsigned) and float).
µ¶
Disadvantages´³• Difficult page control. It is difficult to settle map size and scale. There are a relatively
few color variations although you can set some R-G-B colors.

µ

1.1• Large file size of output figure. An output file supports .eps format but in almost raster
data.

´

Install

GrADS is distributed freely via the Internet. The latest version is 2.0 (The older versions are alsoavailable for old X-windows servers). Unless you want to optimize, customize etc., it is easy to use
pre-compiled version.
At first, download the following files to operate GrADS from http://www.iges.org/grads/downloads.html.
• GrADS software (Choose one for your using OS)• Supplementary Files (data.tar.Z)• Supplementary Scripts (http://www.iges.org/grads/gadoc/library.html)
For the supplementary scripts, you can download all files by wget command.
[hoge@hoge src]$ wget -nc -np -r ftp://grads.iges.org/grads/scripts/
Next, uncompress and extract the downloaded files, and install GrADS. For the GrADS software,[hoge@hoge src]$ tar xvfz grads-2.0.a3-bin-i686-pc-linux-gnu.tar.gz
grads-2.0a3/
grads-2.0a3/bin/grads-2.0a3/bin/bufrscan
grads-2.0a3/bin/gribmap
1...
grads-2.0a3/bin/grads...
grads-2.0a3/COPYRIGHT
grads-2.0a3/INSTALL[hoge@hoge src]$ cd grads-2.0a3/bin[hoge@hoge bin]$ su
Password:[hoge@hoge bin]# cp * /usr/local/bin[hoge@hoge bin]# exit
For the supplementary (coast lines and fonts) files,[hoge@hoge src]$ mkdir grads[hoge@hoge src]$ cd grads[hoge@hoge grads]$ tar xvfz ../data.tar.Z[hoge@hoge grads]$ cd ../[hoge@hoge src]$ su
Password:[hoge@hoge src]# cp -r grads/ /usr/local/lib/[hoge@hoge src]# exit
For the supplementary scripts,[hoge@hoge src]$ cd grads.iges/org/grads/scripts/[hoge@hoge scripts]$ su
Password:[hoge@hoge scripts]# cp * /usr/local/lib/grads/[hoge@hoge scripts]# exit
Add the environmental variable for GrADS.
[hoge@hoge hoge]$ vi .bashrc## For GrADS (for >=2.0)
export GADDIR=/usr/local/lib/grads## for GrDAS (for <2.0)
export GASCRP=/usr/local/lib/grads[hoge@hoge hoge]$ vi .cshrc## For GrADS (for >=2.0)
setenv GADDIR /usr/local/lib/grads## For GrADS (for < 2.0)
setenv GASCRP /usr/local/lib/grads

1.2

Using GrADS gridded data file

The data description file for GrADS is called as “.ctl”. You may understand how the binary datais stored when you see the .ctl file. Get example.tar.Z (you may get the under part of
http://www.iges.org/grads/gadoc/downloads.html) and confirm the data descriptions.
2[hoge@hoge GrADS]$ tar xvfz example.tar.Z
model.ctl
model.dat
sample[hoge@hoge GrADS]$ cat model.ctl
DSET^model.dat
OPTIONS little_endian
UNDEF -2.56E33
TITLE 5 Days of Sample Model Output
XDEF 72 LINEAR 0.0 5.0
YDEF 46 LINEAR -90.0 4.0
ZDEF 7 LEVELS 1000 850 700 500 300 200 100
TDEF 5 LINEAR 02JAN1987 1DY
VARS 8PS
099
Surface Pressure
U
799
U WindsV
7
99
V WindsZ
7
99
Geopotential HeightsT
7
99
TemperatureQ
599
Specific HumidityTS099
Surface TemperatureP099
Precxipitation
ENDVARSIn this example, the binary data set is named model.dat, the undefined, or missing , data value is−2.56E33, there are 72 grid points in the X direction, 46 in the Y direction, 7 levels, 5 times, and 8
variables. The variables U, V, Z, and T have 7 levels, the variable Q has 5 levels, and the variables
PS, TS, and P has one level. The first dimension of these arrays vary from west to east, the second
from south to north.
In this example the horizontal grids are Band Sequential (BSQ) and written in the following order:(02Jan1987, surface, PS), (02Jan1987, 1000 hPa, U), (02Jan1987, 850 hPa, U), ... (02Jan1987, 100hPa, U), (02Jan1987, 1000 hPa, V), ... (02Jan1987, surface, TS), 02Jan1987, no demension, P),(03Jan1987, surface, PS), ... etc.
The further explanation of model.ctl is described as follows. See more information at Using GrADS
Data Files (Section 4.0 in The Users Guide) or at About GrADS Gridded Data Sets in the online
version of the users guide (http://www.iges.org/grads/gadoc/descriptorfile.html).
DSET data-set-name The name of the binary data set is “model.dat”. “^” is the proxy of a fullpath if the binary data set is in the same directory as .ctl file.
OPTIONS Byte order is little endian. If the format of a binary data is big endian, write “big endian”.
UNDEF value The undefined, or missing, data value is -2.56E33. GrADS operations and graphics
routines will ignore data with this value from this data set.This is a required parameter even if
there are no undefined data.

3

TITLE string This data is 5 days of sample model output. You can write a brief description of thecontents of the data set.
XDEF number LINEAR start increment The number of pixels (grid values in the X direction)is 72. The starting longitude is 0.0◦ with 5.0◦ increment from west to east. If the X dimension
values go from east to west, write “xrev” at OPTIONS section.
YDEF number LINEAR start increment The number of lines (grid values in the Y direction)
is 46. The starting longitude is -90.0◦ with 4.0◦ increment from south to north. If the Y
dimension values go from north to south, write “yrev” at OPTIONS section.
ZDEF LEVELS value-list The number of arbitrary pressure levels are 7; 1000, 850, 700, 500, 300,
200, and 100 hPa.
TDEF number LINEAR start-time increment The number of times in the data set is 5. The
start date is 2nd January, 2002 with 1 day increment (i.e. 2, 3, 4, 5, and 6 January, 2002).
VARS number This part indicates the start of the records describing the variables in the data set.
There are eight variable records in this data, the first line with the following format:
abrev levs units description PS is abbreviation for this variable that is “Surface Pressure” (written at description part). A lev value of 0 indicates this variable has one “level” that does notcorrespond to a vertical level. If there are some vertical levels, the number of levels are described.
The unit 99 means that the type of this variable is 4-byte float.
ENDVARS This ends the GrADS data description file.

[Exercise 1 ] Go through the sample ([hoge@hoge GrADS]$ cat sample) or the Tutorial(http://www.iges.org/grads/gadoc/tutorial.html) to get a feeling for how to use the basic
capabilities of GrADS.

4

2

MTSAT

2.1

Outlines

The Multi-functional Transport Satellite - 1 (Replacement) (MTSAT-1R) is a geosynchronous satellite at 140◦ E launched on 26 February 2006 to fulfill a meteorological function and an aeronautical
function, and has operated since 28 June 2006. MTSAT-1R was added a new observation channel
in infrared to detect fog during nighttime and to improve observation accuracy of sea surface temperature. The observation channels and their band show following table. The field of view at nadirin visible (VIS) and infrared (IR) channels are about 1 km and 4 km with 10-bit (0–1023) gradation
regardless of the channels.
ChannelBand [µm]

VIS
IR1
IR2
IR3
IR4

0.55–0.90
10.3–11.3
11.5–12.5
6.5–7.0
3.5–4.0

2.2

Extraction of gridded MTSAT-1R data

The Center for Environmental Remote Sensing (CEReS), Chiba University provides gridded datasetof VIS and IR channels on MTSAT-1R. Gridded dataset (format) that we defined are latitude & longitude oriented (to say another word, after geometric correction based on satellite position’s information)format. However, we take into account re-calibration of radiometer (Tbb and Albedo%).
For the purpose of climatological long-term analysis, it is highly recommended that dataset consistof simple as binary (digital number stored), and text-based calibration table (look up table to convert
digital number into physical variables such as Tbb or Albedo%). If new results provide “new calibration table”, they can easily apply to corrected variables by the replacement as of new calibrationtable, without any new calculations.
This part gives an explanation to read gridded MTSAT-1R data provided by CEReS. Each data
file after the decompression for each channel contains a binary file of count number, and ten ASCII
files of header information. The data files are stored in bz2 compression form for VIS and IRs.
Firstly, download one gridded data from the data server of MTSAT-1R (ftp://mtsat-1r.cr.chiba-u.ac.jp/),then confirm the archived data files.
[hoge@hoge MTSAT]$ wget ftp://mtsat-1r.cr.chiba-u.ac.jp/grid-MTSAT-1.01/200
805/200805020530.ir.tar.bz2[hoge@hoge MTSAT]$ tar xvfj 200805020530.ir.tar.bz2
IMG_DK01IR1_200805020530.geoss
IMG_DK01IR2_200805020530.geoss
・・・
hdr_ir4_200805020530_009.txt
hdr_ir4_200805020530_010.txt
Binary data (.geoss) are encoded in unsigned 2-byte values in big endian byte order. Since PC inthis training course is in little endian byte order, re-encode the binary data using (dd) command.
5

[hoge@hoge MTSAT]$ cp IMG_DK01IR1_200805020530.geoss big_endian.geoss[hoge@hoge MTSAT]$ dd if=big_endian.geoss of=little_endian.geoss conv=swab
Printing on the standard output the gridded file both endian, you may find the value from 0 to 1024
in the little endian file.
[hoge@hoge MTSAT]$ od -t u2 big_endian.geoss
0000000 44802 47618 47618 47618 47618 46082 46082
0000020 46082 46082 54274 51714 51714 51714 55810
・・・[hoge@hoge MTSAT]$ od -t u2 little_endian.geoss
0000000
687
698
698
698
698
698
692
0000020
692
692
724
714
714
714
714
・
・
・

692
730

The data files are arrays of size 12000 × 12000 (longitude and latitude in 0.01◦ ) for VIS and 3000
× 3000 (0.04◦ ) for IRs. The range of the data is from 80◦ E to 160◦ W, and from 60◦ N to 60◦ S. The
center of the first cell is 80.005◦ E (80.02◦ E) and 59.995◦ N (59.98◦ N) for VIS (IRs). That of the second
cell is 80.015◦ E (80.06◦ E) and 59.995◦ N (59.98◦ N).
The .ctl file for IR of the gridded MTSAT-1R data are described as follows:[hoge@hoge MTSAT]$ cat IR-count.ctl
DSET^IMG_DK01IR1_200805020530.geoss
OPTIONS yrev little_endian
UNDEF 1023
TITLE MTSAT-1R count number
XDEF 3000 LINEAR 80.02 0.04
YDEF 3000 LINEAR -59.98 0.04
ZDEF 1 LEVELS 1
TDEF 1 LINEAR 05:30Z02MAY2008 1HR
VARS 1CN
0 -1,40,2
Count number
ENDVARSThat for VIS:
[hoge@hoge MTSAT]$ cat VIS-count.ctl
DSET^IMG_DK01VIS_200805020530.geoss
OPTIONS yrev big_endian
UNDEF 1023
TITLE MTSAT-1R count number
XDEF 12000 LINEAR 80.005 0.01
YDEF 12000 LINEAR -59.995 0.01
ZDEF 1 LEVELS 1
TDEF 1 LINEAR 05:30Z02MAY2008 1HR
VARS 1CN
0 -1,40,2
Count number
ENDVARS
6

Draw the IR count number data using GrADS.
[hoge@hoge MTSAT]$ grads...
Landscape mode? (’n’ for portrait):
GX package Initialization: Size = 11 8.5
ga-> open IR-count.ctl
Scanning description file: IR-count.ctl
Data file IMG_DK01IR1_200805020530.geoss is open as file 1
LON set to 80.02 199.98
LAT set to -59.98 59.98
LEV set to 1 1Time values set: 2008:5:2:5 2008:5:2:5
E set to 1 1
ga-> set grads off
ga-> set gxout grfill
ga-> set lon 80 200
ga-> set lat -60 60
ga-> set xlint 20
ga-> set ylint 15
ga-> set xlopts 1 4 0.15
ga-> set ylopts 1 4 0.15
ga-> d CN
Contouring: 200 to 900 interval 100
ga-> run cbarn
ga-> draw title MTSAT-1R IR1(CN) 20080502 0530UTC
ga-> enable print tmp.gx
ga-> printga-> disable print
Hardcopy output file is closed
ga-> !gxeps -c -i tmp.gx -o IMG_DK01IR1_200805020530.eps
ga-> quit
No hardcopy metafile open
GX package terminated[hoge@hoge MTSAT]$ convert -rotate 90 IMG_DK01IR1_200805020530.eps ir1-count.eps[hoge@hoge MTSAT]$ ggv ir1-count.eps
This CN value has no physical meanings. Thus, the CN value should be converted to brightness
temperature using above convert table. The header file (.txt) are described in ASCII. There is aconvert table between count number and brightness temperature (Tb) in the header file.
[hoge@hoge MTSAT]$ cat hdr_ir1_200805020530_001.txt
Header Type #0 - Primary Header
Header_Record_Length : 16・・・
_UNIT:=KELVIN
7

Figure 1: ir1-count.eps

0:=330.06
30:=327.69
60:=325.29
・・・65535:=129.99
・・・
0 bytes left in header
Extract the convert table from the header file using (awk) command.
[hoge@hoge MTSAT]$ awk ’/[0-9]:=/ {print}’ hdr_ir1_200805020530_001.txt | c
ut -d: -f1 > tmpa.txt[hoge@hoge MTSAT]$ awk ’/[0-9]:=/ {print}’ hdr_ir1_200805020530_001.txt | c
ut -d= -f2 > tmpb.txt[hoge@hoge MTSAT]$ paste tmpa.txt tmpb.txt > tbbtable.txt[hoge@hoge MTSAT]$ cat tbbtable.txt0
330.0630
327.6960
325.29・・・65535
129.99CEReS distributes a sample program to convert CN to Tb of gridded MTSAT-1R data. Get it fromthe data server and compile it.
[hoge@hoge programs]$ wget ftp://mtsat-1r.cr.chiba-u.ac.jp/support/c/readMT
SAT-1.01.tar.gz[hoge@hoge programs]$ tar xvfz readMTSAT-1.01.tar.gz
readMTSAT-1.01/
readMTSAT-1.01/MTSAT-tbb.ctl
readMTSAT-1.01/count2tbb.c
readMTSAT-1.01/count2tbb.sh8

readMTSAT-1.01/readme.txt[hoge@hoge programs]$ cd readMTSAT-1.01[hoge@hoge readMTSAT-1.01]$ gcc count2tbb.c -o count2tbb
Move to the directory at the sample data and execute the sample program.
[hoge@hoge MTSAT]$ /home/hoge/programs/readMTSAT-1.01/count2tbb little_endi
an.geoss[Exercise 2 ] Draw the converted Tbb data using GrADS. The .ctl file as follows;[hoge@hoge MTSAT]$ cat IR-tbb.ctl
DSET^tbb_little_endian.geoss
OPTIONS yrev
UNDEF -999.0
TITLE MTSAT-1R Tbb
XDEF 3000 LINEAR 80.02 0.04
YDEF 3000 LINEAR -59.98 0.04
ZDEF 1 LEVELS 1
TDEF 1 LINEAR 05:30Z02MAY2008 1HR
VARS 1TBB
0
99
Tbb [K]ENDVARS

Figure 2: ir1-tbb.eps

CEReS does not only provides gridded geostationary satellites data of MTSAT-1R, but also those of
the Fengyun (FY) -2C and -2D launched by China Meteorological Administration (located at 105◦ E
and 86◦ E, respectively) and the Geostationary Operational Environmental Satellite (GOES) launchedby the National Oceanic and Atmospheric Administration (NOAA) (located at 135◦ W and 75◦ W).
Although there are some differences how to convert CN to Tbb depending on the satellites, you may
read them easily. See each README file and sample program on the data server or refer to “CEReS
4VL Wiki” (http://www.cr.chiba-u.jp/~4vl/ but in Japanese text only).
9

[Exercise 2 ] Make longitude-time section in IR1 Tbb at 0◦ in latitude from 90◦ to 180◦ in May
2008.
¶
Procedure³

1. Download the gridded IR data in May 2005.
2. Convert CN files to Tbb ones.
3. Draw it by GrADS utilizing OPTIONS temprate in .ctl file.
µ´[hoge@hoge exercise2]$ cat exercise2.ctl
DSET ^%y4%m2%d2%h230.ir1tbb.geoss
OPTIONS yrev temprate
UNDEF -999.0
TITLE MTSAT-1R Tbb
XDEF 3000 LINEAR 80.02 0.04
YDEF 3000 LINEAR -59.98 0.04
ZDEF 1 LEVELS 1
TDEF 744 LINEAR 00Z01MAY2008 1hr
VARS 1TBB
0
99
Tbb [K]ENDVARS[hoge@hoge exercise2]$ grads...
ga-> open exercise2.ctl
ga-> set lat 0 0
ga-> set lon 90 180
ga-> set t 1 745
ga-> set xlint 15
ga-> set xlopts 1 4 0.15
ga-> set ylopts 1 4 0.15
ga-> set gxout grfill
ga-> set grads off
ga-> d tbb
ga-> run cbarn
ga-> draw title MTSAT-1R IR1 Tbb Lon.-Time Section at 0 deg
ga-> run cbarn

3

NOAA AVHRR

3.1

Outlines

The Television InfraRed Operational Satellite - Next-generation(TIROS-N/NOAA) series of polar
orbiting meteorological satellites. The multiple NOAA satellites always operate (presently NOAA 17
10

Figure 3: crosssection.eps

and 18). Each satellite observes at least two times in a day at a certain point with near-constant solar
time. The orbit altitude is 830 and 870 km and that frequency is about 100 minutes. The NOAAs
carry the Advanced Very High Resolution Radiometer (AVHRR) to observe cloud and land covers
which data use in this program, in addition, the TIROS Operational Vertical Sounder (TOVS), theAdvanced Microwave Sounding Unit (AMSU), and the Solar Backscatter Ultra-Violet Experiment(SBUV). The observation range and resolution of the AVHRR are 2700 km and 1 km, respectively.
Channels

Band [µm]Red
Near-infrared
Thermal-infrared 1
Thermal-infrared 2
Thermal-infrared 3

0.58–0.68
0.725–1.1
3.55–3.93
10.30–11.30
11.50–12.50

3.2

Extraction of gridded NOAA AVHRR data

The NOAA AVHRR products provided by CEReS are available from the Internet. The gridded
data are archived in .bz2 format for each observation time.
At first, download an archived NOAA AVHRR data from ftp://avhrr.cr.chiba-u.ac.jp/, extract it, and confirm the contents.
[hoge@hoge AVHRR]$ wget ftp://avhrr.cr.chiba-u.ac.jp/disk2/products/200710/
n1807100204.tar.bz2[hoge@hoge AVHRR]$ tar xvfj n1807100204.tar.bz2
n1807100204.mb1.gi : Redn1807100204.mb2.gi : Near-infraredn1807100204.mb3.gi : Thermal-infrared1
n1807100204.mb4.gi : Thermal-infrared1
n1807100204.mb5.gi : Thermal-infrared1
n1807100204.sza.gi : Solar zenith angle

11n1807100204.saa.gi : Solar azimuth angle
n1807100204.sca.gi : Scan angle
n1807100204.nvi.gi : NDVIn1807100204.sst.gi : SSTNOAA AVHRR products data (.gi) consist of header part (80 bytes), data part (70,948,872 byte),
and footer part. In the data part, each variables are written as 2-byte integer in big endian. Eachdata are corresponds to the physical values as follows:• Red and Near-infrared: Albedo (0.0, 100.0%) == (0, 1000)• Thermal-infrared 1–3: Brightness Temperature (0.0, 320.0 K) == (0,3200)• Solar zenith angle: (−360◦ , +360◦ ) == (−3600, +3600)• Solar azimuth angle: (−360◦ , +360◦ ) == (−3600, +3600)• Scan angle, SST: (0.0, 320.0 K) == (0, 3200)• NDVI: (−1.0, +1.0) == (−100, +100)
Confirm the header part using sample program.
[hoge@hoge AVHRR]$ /home/hoge/programs/AVHRR/readheader n1807100204.mb4.gi
File name n1807100204.mb4.gi(1) Number of lines per band
: 5562(2) Number of bands in the image:
1(3) Number of bytes per line
: 12756(4) Number of label records:
0(5) Number of bytes in one label record :0(6) Data_organization:
2(7) Data type:2(8) Starting_byte_number:
80(9) p file identifier: imagNote: Data type 1:byte,2:integer*2,3:integer*4,4:real*4,5:real*8,6:complex*
8,7:complex*16
The data files are arrays of size 6378 × 5562 (longitude and latitude in 1 km) from west to east, andnorth to south. The range of the data is from 100◦ E to 170.022095◦ E, and from 60◦ N to 9.97971◦ N.
The .ctl in this format:[hoge@hoge exercise2]$ cat avhrr.ctl
DSET ^n1807100204.mb1.gi
OPTIONS yrev little_endian
UNDEF 0
TITLE AVHRR Red
XDEF 6378 LINEAR 100 0.01097869
YDEF 5562 LINEAR 19.97971 0.00899322
ZDEF 1 LEVELS 1
12

TDEF 1 LINEAR 04Z02OCT2007 1hr
VARS 1RED
0 -1,40,2 Tbb [K]
ENDVARS[Exercise 3 ] Draw the other band and products referring to above .ctl file.

Figure 4: n1807100204.mb1-albedo.eps

This product does not take any atmospheric correction and sensor degradiation correction sincethese corrections do not incorporate a product making routine developed by Global Imaging Corp.
Thus, you should pay attention the characteristics of the data.

13

TRMM Data Handling
Takuji Kubota
Earth Observation Research Center (EORC)

Japan Aerospace Exploration Agency (JAXA)Contents
TRMM Data Overview
Analyzing Tools
Tutorial for PPS Orbit Viewer

2

TRMM Data Overview

3

Data Processing for Standard Products
Raw data

Transferred from TRMM

Level 0Quality checked raw data

Level 1

Calibrated data (PR received
power etc.)

Level 2

Geophysical parameters (rain rate,etc.)

Level 3

Temporary and spatiallyaveraged (monthly mean rainfall)

InstantaneousMonthly, or 3-hourly

4Standard Product Naming Rules
2

ProcessingLevel(0 - 3)

A

Level Type(A - C)2

5

Sensor （0 - 5）
0: VIRS
1: TMI
2: PR
3: TRMM Combined
4: TRMM & Other Sources
5: GV

Algorithm
Number（1 - 6）

Point CERES and LIS products

have different naming rules
because they are processedand distributed by otherNASA Centers.
5

Examples of Level 1-2 Products
Data Product

Description

1B01: Visible and Infrared
Radiance (VIRS)

Calibrated VIRS (0.63, 1.6, 3.75, 10.8, and
12 um) radiances at 2.2 km resolution over a
720 km swath

1B11: Microwave
Brightness Temperature(TMI)

Calibrated TMI (10.65, 19.35, 21, 37, and85.5 GHz) brightness temperatures at 5 to 45
km resolution over a 760 km swath

2A12: Hydrometeor Profile(TMI)

TMI Hydrometeor (cloud liquid water, prec. water,
cloud ice, prec. ice) profiles in 14 layers at 5 km
horizontal resolution, along with latent heat andsurface rain, over a 760 km swath

2A25: Radar Rainfall Rate andProfile (PR)PR (13.8 GHz) rain rate, reflectivity, andattenuation profiles, at 4 km horizontal, and 250 m
vertical, resolutions, over a 220 km swath

6Level and Data Array0

end start
0

Level 1 and Level 2

Level 3040N

scaling up

40S

Data is set insequence as TRMM
observed for eachorbit.

0

Data is set in certain grid size in globalarea, averaged in certain period.

GSMaP dataset in the afternoon session is
Level 3 product.

7

Data Format
 HDF-EOS Format
HDF（Hierarchical Data Format）, Specifiying
Satellite observation.

 Structure of HDF Format
Metadata + Scientific Data Sets (SDS) + V
data
Category

Characteristics

ExampleMetadata

Detailed description of file in text
formatalgorithm version,
pixel count number,
etc.

SDS

（Scientific Data Sets）

multi-dimension matrix dataconsisting from data elements insame typeradar reflectivity，
rain

Vdata

table data which can combine
data in different type

data by each scan8

Note for data handling (1)maneuverOrbit maintenance maneuver (Delta-V maneuver)
To keep satellite altitude at appropriate range.
Currently, operated once in 7-10 days.

180 degree yaw maneuver
To avoid VIRS radiation cooling unit to go sun side.
Operated once in 2-4 weeks
Point90 degree yaw maneuver

Since satellite flying direction is reversed at thismaneuver, scan direction is also reversed. Need tocheck geolocation of each scan data in the same time.

To assess PR cross-track antenna pattern.
Operated once in half year.

9180-degree Yaw ManeuverFlying direction

Flying direction

1
2

3

760km
3

2 1
10

Note for Data Handling (2)Other factor for erroneous observation
Radio interference
example in Australia, etc.

Natural phenomena

Point

PR is not observe thesearea because the same
frequency to PR is used in
the ground.

Leonid meteor shower, etc.

Sensor maintenance
operated in non-observation modeTrouble in solar array panel
VIRS stopped operation from 6 Sep. to 17 Oct. 2002

11

Note for Data Handling (3)Handling of missing data
Each missing scan filled by “Missing Value” to indicate they
are mission data
Data
type

1-byteinteger

2-byteinteger

4-byteinteger

4-bytereal

8-byterealMax. ofmissingvalue※

-99

-9999

-9999

-9999.9-9999.9※ Numeric value lower than upper indicates mission data or invalid

When all data in a orbit is missing, scan data is omitted and “Orbit Size”
in Metadata element will be “0”.
12

Note for Data Handling (4)TRMM altitude change
At Launch: about 350km
After Aug.2001 : about 403 km
Characteristics of TRMM Instruments
Swath Width (km)Ground Resolution (km)Pre-boost

Post-boost

Pre-boost

Post-boost

VIRS

720

833

2.2

2.4

TMI

760

878

4.4*

5.1*

PR

215

247

4.3

5.0

* Ground resolutions of TMI are those at 85.5 GHz (highest resolution).

13

Note for Data Handling (4)TRMM altitude change
Observation in August 2001 is;Altitude oforbit

Start date &orbit number

End date &orbit number

Data distribution

350km

2001/8/1
21164

2001/8/7
21267

YES

Altitude rising
period2001/8/7
21268

2001/8/24
21540

Empty granule402.5 km

2001/8/25
21541

2001/8/31
21649

YES

PR Level 3 product for August 2001 was made from about 13
days (1-7 Aug. and 25-31 Aug.) eliminated data during altitude
rising period
Point

Note that using data in different version number.
Version number is confirmed from file name.
14Analyzing Tools

15

How to Get the TRMM Online Data (1)PR/TMI/VIRS Data
DAAC (Goddard Distributed Active Archive Center - NASA/GSFC)
http://disc.sci.gsfc.nasa.gov/data/datapool/TRMM/
Level 1, 2, 3 standard products available
Precipitation Processing System (PPS) - NASA/GSFC
http://pps.gsfc.nasa.gov/
PPS Toolkit, HDF information
Gridded text data (3G68) available(ftp://trmmopen.gsfc.nasa.gov/pub/)
LIS (Home Page/download data)
http://thunder.msfc.nasa.gov/
TRMM Realtime System
http://pps.gsfc.nasa.gov/trmmrt/index.htm
TRMM 3 Hourly Global Rainfall - NASA/GSFC
http://trmm.gsfc.nasa.gov/
Near real-time 3-hourly rainfall data (3B40RT, 3B41RT, 3B42RT)

16

How to Get the TRMM Online Data (2)EOC home page - JAXA/EOC
http://www.eorc.jaxa.jp/en/about/distribution/index.html
Satellite information and images, data order

EORC TRMM home page - JAXA/EORC
http://www.eorc.jaxa.jp/TRMM/index_e.htm
TRMM data in Asia monsoon and Japan area
Tropical cyclone database
http://sharaku.eorc.jaxa.jp/TYP_DB/index_e.shtml

TRMM Realtime Tropical Cyclone Monitoring (images only)
http://sharaku.eorc.jaxa.jp/trmm/RT/index_e.html

GSMaP_NRT home page - JAXA/EORC
http://sharaku.eorc.jaxa.jp/GSMaP/index.htm
GSMaP Data Handling will be introduced in the afternoon session

17

JAXA/EORC Tropical Cyclone Database(for TRMM, AMSR-E and AMSR)
http://sharaku.eorc.jaxa.jp/TYP_DB/index_e.shtml
Browse images, 3D movies
and data of tropical
cyclones observed by
TRMM, Aqua/AMSR-E,
Midori-II/AMSR are
available.

18

TRMM Analysis Tool
PPS/TSDIS Orbit Viewer

For quick look. Easy to make browse image.
For UNIX, Linux and Windows.
http://pps.gsfc.nasa.gov/tsdis/TSDISorbitViewer/rele
ase.html
Demonstration later.

HDF Group (Univ. of Illinois)

C and FORTRAN library for reading and writing HDF format
data.
For UNIX and Linux.
http://www.hdfgroup.org/products/hdf4_tools/

PPS/TSDIS Toolkit

C and FORTRAN library based HDF Library.
For TRMM data (PR,TMI,VIRS,COMB,GV) only.
For UNIX and Linux.
http://pps.gsfc.nasa.gov/tsdis/tsdistk.html

19Tutorial for
PPS Orbit Viewer

20Sample Image of Orbit Viewer

21

Let‟s practice ! (1)
1. Start orbit viewer.
2. Open
TRMM_orbit/2A25.071005.56330.6.HDF.
3. Select „e_SurfRain‟ in Arrays.
4. Click east of Philippines in the bottom
panel.
5. Click anywhere in the PR path.

6. You can select „Option‟- „Zoom
resolution‟, or „Interpolate Data‟.

22

Let‟s practice ! (2)
1. Select „rain‟ in Arrays.
2. Click east of Philippines in the bottom
panel.
3. Control „vertical layer slider‟. (“1” is aspace side layer, 80 is a ground sidelayer)
Vertical layer slider(ex., „80‟=ground,„20‟=5km altitude,‟40‟=10km altitude)23

Let‟s practice ! (3)Select the „View‟- „Array‟„Fixed_Cross_Section‟ menu item, and a
new window will appear.
Select „Along Track‟.
To change the displayed element of the
fixed dimension, move the slider at thebottom.
„Level 39‟ means
about 10km altitude.

Slider24

Let‟s practice ! (4)Select the „View‟- „Array‟- „3D_Viewer‟ menu
item, and a new window will appear.
Click „Select data‟.
Try to menu sliders of the 3D Control Window.
3D Control Window

View
controlsliders

Threshold of rain rate
25

Let‟s practice ! (5)Many sample data are set up. Try to open
another data using the Orbit Viewer.

26

Look at Data Sample (1)In the directry „TRMM_orbit‟,
1B01.071005.56330.6.HDF(VIRS)

1B11.071005.56330.6.HDF(TMI Brightness Temperature)
2A12.071005.56330.6.HDF(TMI rain rate)
2A25.071005.56330.6.HDF(PR Rain rate)
3A25.071001.6A.HDF(Monthly PR Rain rate)
27

Look at Data Sample (2)“TC_DB” folder includes 12 sample
data of TRMM archived at JAXA/EORC
Tropical Cyclone Database(http://sharaku.eorc.jaxa.jp/TYP_DB/index_e.shtml)

1B01...HDF (VIRS)
1B11...HDF (TMI Brightness Temperature)
2A12...HDF (TMI rain rate)
2A25...HDF (PR Rain rate)

See images in TC_DB/list for yourreference.

28

TC_DB List (1)29TC_DB List (2)30GSMaP Data Handling
Takuji Kubota
Earth Observation Research Center (EORC)

Japan Aerospace Exploration Agency (JAXA)Contents
GSMaP Data Overview
Handling of Precipitation data

2

GSMaP Data Overview

3Global rainfall map by multi-satellites
Global Rainfall Map in Near Real Time by JAXA/EORC
http://sharaku.eorc.jaxa.jp/GSMaP/
We have started to release hourly global rainfall data (0.1x0.1deg. lat/lon) innear real time (about four hours after observations) and visualize the latest
data quickly.
Movie Button

30/09/2008

T. Kubota, presented at AIT, Bangkok

Google Earth Button

4

How to Get the GSMaP Online Data
GSMaP_NRT (NRT:Near-real-time) by
JAXA/EORC
http://sharaku.eorc.jaxa.jp/GSMaP/index.htm
Details will be presented later.

GSMaP Data Archive Center
http://www.gsmap.aero.osakafu-u.ac.jp/
GSMaP Standard version (post-processing)
Period: 1998-2006

5

GSMaP_NRT: Near-Real-time Data
JAXA’s site
Data will be archived about 4 hours after
observation.
Ex., Data of 00:00-0:59UTC will be put at about 5 UTC.

FTP
ftp hokusai.eorc.jaxa.jp

When you want to use GSMaP data after IHPTraining Course, please send your e-mail totrmm_real@jaxa.jp
Data are free ! We just need your registration.

6

Basic Information
Temporal resolution
1 hour (hourly data)

Grid resolution
0.1 degrees latitude/longitude grid
10km at the equator

DomainGlobal (60N-60S)

Data latency
4 hours after observation

Data Period
1 December 2007 – present in JAXA site
Data during 1998-2006 is archived in GSMaP Data Archive
Center.
7

File Naming Rules
Data and flag files are named according to thefollowing rules;
Hourly Rain Rate data:
gsmap_nrt_YYYYMMDD.HHNN.dat

Satellite Information Flag:
gsmap_nrt.YYYYMMDD.HHNN.sateinfo.dat

Observation Time Flag:
gsmap_nrt.YYYYMMDD.HHNN.timeinfo.dat

where,
YYYY: 4-digit year
MM: 2-digit month
DD: 2-digit day
HH: 2-digit hour
NN: 2-digit minute (currently fixed as 00)

8FTP Information
FTP Directory Information
Hourly Rain Rate data;
Latest 24 hour data:/realtime/latest/

Archive:/realtime/archive/YYYY/MM/DD/

Satellite Information Flag;/realtime/sateinfo/YYYY/MM/DD/

Observation Time Flag;/realtime/timeinfo/YYYY/MM/DD/

9

Data Format
All binary files
little-endian byte order platformarchived with compressed using “gzip”.

Grid of those files
3600 x 1200 pixels
longitude-latitude elements corresponding to a 0.1 x
0.1 degree grid that covers the global region from 60N
to 60S.
The center longitude and latitude of the first pixel[1,1] (left top corner) is [0.05E, 59.95N] (see next
slide).

File Size
Approximately 800 Kbyte (with gzip), and 17 Mbyte(uncompress).
10Data Coverage Map
The first pixel is on left-top corner [1,1]:(0.05E, 59.95N)

3600 pixels

1200 pixels

The last pixel is on right-bottom corner [3600:1200]:(0.05W, 59.95S)

Data Coverage Map (Rain and Flag data)11

Definition of Missing Data
“Hourly Rain Rate” data are stored in 4-byte float plain binary format.
Unit is [mm/hr].
Negative value denotes missing in observation data or no retrieval wasdone in microwave retrieval algorithm
Value(positive)

Description
Hourly rain rate [mm/hr].

−4Missing due to low temperature in microwave retrieval algorithm.

-8

Missing due to sea ice in microwave retrieval algorithm.

−999

Missing due to no observation

12

Satellite Information Flag
Satellite Information Flag:
gsmap_nrt.YYYYMMDD.HH
NN.sateinfo.dat

Each bit in flag responses
to satellite/sensor
respectively
value of flag indicatesaccumulated value of all
satellite and/or sensor
name.

Pixel Value
Value

Description

Bit

Sensor Category

Satellite/Sensor

1

0

TRMM/TMI

2

1

Aqua/AMSR-E

4

2

8

3

DMSP-F14/SSM/I

16

4

DMSP-F15/SSM/I

65536

16

GOES-EAST

13107217

GOES-WEST

Microwave radiometer
aboard low orbital
satellite

Infrared Imager aboard
Geo-stationary
meteorologicalsatellite

DMSP-F13/SSM/I

26214418

INDEX

524288

19

METEOSAT

1048576

20

MTSAT−(negative)

31

No microwave radiometer observation

13

Observation Time Flag
Observation Time Flag:
gsmap_nrt.YYYYMMDD.HH
NN.timeinfo.dat“Observation Time
Information Flag”
indicates relative time oflatest microwave
radiometer observation ateach pixel.
0 means start time of thefile (HH in file name).
Values are stored asindicated in right table.

Value

Description+X

If value is positive, microwave radiometer
observation is available at the pixel during
time period of the file. X (0 ≦ X < 1)indicates relative observation time of latest
microwave radiometer, and is stored as
differences from the start time of the file.
For example, if UTC of the file (HH) = “01”and X = 0.2, observation time of the pixel
will be 01:12 UTC.

−Y

If value is negative, NO microwave radiometer
observation is available at the pixel during
time period of the file. Y indicates relative
observation time of latest microwave
radiometer, and stored as differences fromthe start time of the file.
For example, if UTC of the file (HH) = “01”and Y = −2.5, latest observation time of
microwave radiometer at the pixel will be
23:30 UTC of previous day.

−999

Missing data.

14

GrADS Control File
Sample control files of the Grid Analysis
and Display System (GrADS) for eachproduct are also available from ftp
server.
Hourly Rain Rate data:/realtime/archive/GSMaP_NRT.hourly.rain.ctl

Satellite Information Flag:/realtime/satinfo/GSMaP_NRT.hourly.sat.ctl

Observation Time Flag:/realtime/timeinfo/GSMaP_NRT.hourly.time.ctl

15

Summary of GSMaP_NRT Products
Parameter[unit]

Data format

1

Hourly Rain
Rate [mm/h]

4-byte float
plain binary,
little-endian

2

Satellite
Information
Flag

4-byte singed
integer plain
binary, littleendian3

Observation
Time Flag

4-byte float
plain binary,
little-endian4

Hourly Rain
Rate in text
format[mm/h]ASCII,
CSV format

No

Coverage

Grid size

Horizontal
resolution

6

4-byte float
plain binary,
little-endian

FTP directory
Latest 24-hr:/realtime/latest/
Archive:/realtime/archive/
YYYY/MM/DD/

Global (60°N60°S)

3600
x 12000.1 degree
grid box

Hourly/realtime/sateinfo/
YYYY/MM/DD//realtime/timeinfo/
YYYY/MM/DD//realtime/txt/AAABBBB/YYYY/MM/DD/

200 rows
x 400 lines

5
Daily
Accumulated
Rainfall[mm/day]

Temporal resolution

Global (60N60S)

1440
x 480

0.25 degree
grid box

Daily(accumulation from 00Z to
23Z of the specified day)/realtime/daily/00
Z-23Z/YYYYMM/

Daily(accumulation from 12Zof previous day to 11Z ofthe specified day)/realtime/daily/p1
2Z-11Z/YYYYMM/

16

Handling of Precipitation
data

17

Mission List
Rainfall gridded Data is put in Directry ‘GSMaP_DATA’
Draw a figure of global distribution of rainfall by GrADS.
Open GrADS control file (GSMaP_NRT.hourly.rain.ctl) by GrADS and draw it by acommand ‘d precip’.
open GSMaP_NRT.hourly.rain.ctl
set gxout grfill
d precipDraw a figure of your favorite area, for example, 80E-90E, 17N-27N.
Examle;set lon 80 90
set lat 17 27
d precip

Compute a daily mean by GrADS and draw a figure of a daily mean.
Example;set lon 80
set lat 17
set t 1 24
d precipDraw a figure of time series at your favorite location.

18

Satellite data/images
access through
JAXA/EORC www/ftp site
Misako KACHI
Earth Observation Research Center (EORC)
Japan Aerospace Exploration Agency (JAXA)

10 Nov 2008

The Eighteenth IHP Training Course

Contents
JAXA Introduction (DVD video - 14min)
JAXA's water related missions

Tropical Rainfall Measuring Mission (TRMM)
Advanced Microwave Scanning Radiometer for EOS-Aqua (AMSR-E)
Advanced Land Observing Satellite (ALOS)
Future missions: GCOM-W/-C, GPM

Satellite data access to TRMM/AMSR-E data via www/ftp site
TRMM/AMSR-E standard products
JAXA/EORC Tropical Cyclone Database
TRMM Tropical Cyclone Monitoring
Global Precipitation Map in Near Real Time
Latent Heating Research Product
OthersJAXA activities for hydrological applications

Collaboration with IFNet/ICHARM
Collaboration with University of Tokyo
Sentinel Asia/Sentinel Asia for Environment (DVD video, if there is enoughtime)

Appendix: List of TRMM www/data sites

2

GEOSS 10yr Implementation Plan
GEOSS
Earth System
Models

Decision
SupportPrediction andAnalysis・Oceans
・Cryosphere・Land・Atmosphere・Solid Earth・Biosphere

9 Societal Benefit Areas

・Assessment
・Decision Support
Systems

High Performance
Computing;Communication
Visualization

PolicyDecisions

Other Data
Sources

Assimilation

Management
Decisions

Socio-economic data

Earth Observation
Systems・In Situ
・Airborne

Standards &Interoperability

Observations

Disasters
Weather
HealthEcosystems

Ongoing feedback to optimize value,
reduce gaps, and account for humanactivity

Energy・Space-based

U.S.A

Agriculture

Japan
ALOS

EarthCARE

GOSAT

EuropeClimateGPM/DPRGCOM

NPOESS
DecadalSurveyMissions

…..

METOP
Sentinels

disaster

Vegetation……
GHG

Precipitation

Cloud,
AerosolWind speed
SST

Biodiversity

Water
Red: Japanese priority areas

JAXA’s water related missions
TRMM (1997~)
Precipitation Radar (PR)TRMM Microwave Imager (TMI)

ADEOS-II (2002~2003)
Global Imager (GLI)
Advanced Microwave ScanningRadiometer (AMSR)
SeaWindsAqua (2002~)
Advanced Microwave ScanningRadiometer for EOS (AMSR-E)

ALOS (2006~)

Tropical Rainfall Measuring Mission(TRMM)
Major characteristics

Focused on rainfall observation.
First instantaneous rainfall
observation by three different
sensors (PR, TMI, VIRS). PR,
active sensor, can observe threedimensional structure of rainfall.
Targeting tropical and subtropicalregion, and chose non-sunsynchronous orbit (inc. angle 35
degree) to observe diurnal
variation.

US-Japan joint
missionJapan: PR, launch
US: satellite, TMI,
VIRS, CERES, LIS,
operation

Launch28 Nov. 1997 (JST)

Altitude

About 350km (since 2001,
boosted to 402km to extend
mission operation）

Inc. angle

About 35 degree, non-sunsynchronous orbit

Design life

3-year and 2month (still
operating)Instruments

Precipitation Radar (PR)TRMM Microwave Imager (TMI)
Visible Infrared Scanner (VIRS)
Lightning Imaging Sensor (LIS)
CERES (not in operation)

Major achievement in Japan

Demonstration of high quality andhigh reliability of a satellite
onboard precipitation radar
Improvement of MWR precipitation
retrieval by PR 3D observation
Pioneering precipitation system
climatology by PR observation
Operational use in NWP etc.
New products including all-weather
SST, global soil moisture5

Observation of 3-dimensional Structureof Tropical Cyclones
Hurricane Katrina in 2005
PR & IR image

PR 3D

TMI image6

Tropical cyclone observation
impact in forecasts
Rainfall by Precipitation
Radar (color) + cloud image(grey scale)

Rainfall by Microwave
radiometer

Hurricane KENNA
in 2002

Structure of "eye" of hurricane (denote in +) is not recognized in cloud image (left),but clearly seen in microwave radiometer observation (right). In consequence, 24-hr
forecast regarding intensity by NOAA Tropical Prediction Center was corrected.
7

TRMM PR climatology

8Advanced Microwave ScanningRadiometer for EOS
 Mission status

 Continuous observation over 6-years
after the launch on May 4, 2002
onboard NASA’s EOS Aqua satellite.
 Stable brightness temperature records,
except the loss of 89GHz-A data from
November 2004.
 Operation of the Aqua satellite will bemaintained at least until 2011,
according to NASA senior review.

 Instrument characteristics

 Multi-frequency microwave radiometerwith dual polarization capability(developed by JAXA).
 High-spatial resolution compared toexisting instruments by large size
antenna.
 C-band (6.9GHz) channels for
estimating SST and soil moisture.
 Afternoon (1:30 pm) equatorial crossing
time that is currently unique formicrowave radiometers.

Pre-launch AMSR-E in TsukubaSpace Center

Soil Moisture observation
 AMSR-E soil moisture anomalies over south-eastern
Australia (green rectangle) averaged Sep.-Oct. 2005 and2006 are compared to wheat production.

2005

Soil Moisture
2006

Wheat production
Source: Australian Bureau of Agricultural and Resource Economics

All weather SST
 C-band (7GHz) channels are indispensable for retrieving global
sea surface temperature and soil moisture. All-weather, frequent
measurements enables analyses of rapid changes of SST.
 Time-proven infrared measurement and microwave observations
are in complementary situation in terms of spatial resolution anderror sources.
 AMSR-E and various infrared sensor data are merged by objective
analysis to obtain cloud-free high-resolution SST image.
GLI SST on April 10, 2003

AMSR SST on April 10, 2003

SST images around east coast of Japan on April 10, 2003 obtained by
GLI (left) and AMSR (right). Differences of spatial resolution andinfluence of clouds (lower-left white areas in GLI image) are clearlyseen.

New Generation Sea Surface Temperature(NGSST) by NGSST group (led byProfessor Hiroshi Kawamura of Tohoku
University).

SST anomaly in northern high latitudes

Advanced
Land Observing Satellite (ALOS)
ALOS“Daichi”(Advanced Land Observing Satellite)

Data Relay Antenna (DRC)[Data rate: 240Mbps]
Star Tracker
GPS Antenna

PALSAR
8.9m

Jan. 24, 2006： Launch by H-IIA #8 from TNSC
Aug. 30, 2007： 1.6 year (583 days) after launch

Mission objectives:- Cartography (1:25,000 scale),- Regional environment observation,- Disaster monitoring, and- Resources surveying.

2.9m

PRISM

Velocity

AVNIR-2

Solar Array Paddle

22m

Nadir
PRISM : Panchromatic Remote-sensing Instrument for Stereo Mapping
AVNIR-2: Advanced Visible and Near Infrared Radiometer type 2
PALSAR: Phased Array type L-band Synthetic Aperture
Radar
Earth
Observation Research Center

EORC

ALOS Characteristics
Launch Date
Orbit
Local Time at DN
Altitude
Inclination
Recurrent Period
Revolution
PeriodLongitude Repeatability
Data Collection

Yaw Steering
Attitude Error each axis
Satellite Mass
Power

10:33am, January 24th, 2006 (JST)Sun-synchronous
10:30am +/- 15 min.
691.65 km @Equator
98.16 degrees
46 days (Sub-cycle: 2 days)
14 + 27/46 (/day), 671 (/recurrent)
98.7 minutes+/-2.5 km @Equator
1 DRTS (Data Relay Test Satellite), 240 Mbps
HSSR (High Speed Solid state Recorder)+ DT (X-band direct downlink), 120 Mbps
Off / On
2.0e-4 degree (determination)
0.1 deg. (maintain)
4,000 Kg
7 KW @EOL

EORC

Earth Observation Research CenterDeforestation Monitoring in Amazon

JERS-1 SAR (Oct., 1996)

ALOS PALSAR (Jun., 2006)★
Red shows deforestation
area during 10years
Above image shows one ofmost popular deforestation site.

100km

10 year image comparison using JERS-1/SAR with ALOS/PALSAR.

EORC

Earth Observation Research Center

Grasp of flooding area at Mekong Delta
R:2006/9/15
G:2006/10/2
B:2006/10/14

2006/10/2
2006/9/15

2006/10/2

2006/10/14

One month・From September 2006 to October 2006, we observed Mekong Delta continuously, using
PALSAR/ScanSAR. Mekong Delta is affected by flooding every rainy season.
・We made color composite 3 season images about enclosed by red rectangle.
・On the composite image, color shows area having any change.
Generally, flooding area appears darker than other area because of weak backscattering.
Thus, we can assume that red area around plain was flooded after September 15 and Yellow areaaround Mekong river was flooded after October 2.
・Continuous observation by ScanSAR enables time series analysis of flooding area. Earth Observation Research Center

EORC

JAXA future environment missions
EarthCARE/

GCOM-W

GCOM-C

CPR

Water cycle

Spatial distribution ofcloud and aerosols
Carbon cycle
and land cover

Atmosphere

3D structure of cloud
and aerosols

Water vapor

Water cycle

GOSAT
Green house
gases

C

n
bo
r
a

e
cl
y
c

Green housegasesdischarge

sink-source

Vegetation
Land coverproduction Soil moisture

Land

Cloud

Aerosol
AerosolCarbonPrecipitationcy
cle
sink-source

Snow Ice Ocean primary

production
Coastal environment

Water cycle

Sea-surfacewind
Sea-surface
temperature

Ocean

Deep ocean circulationPH, nutrients, dissolved CO2

EOS
Decadal survey
missions

3D structure of
precipitation

GPM/
DPR

Other variables:
Ozone
Sea level
Salinity
Sea state...

Sentinel
Earth Explorer

NPOESSMETOPGlobal Change Observation Mission
 Demonstrate long-term global observation of various
geophysical parameters for understanding climate
variability and water cycle.
 Two medium-sized satellites, three generations with one
year overlap to ensure 10-15 years stable data records.
 Cooperation with climate models and direct contribution tooperational users.
Main
responsibilityof JAXAGCOM-W

GCOM-C

Cooperation
with theclimate model
research
institutions

Atmosphere

Input

Cryosphere
Satellite observation

Comparison

Model prediction

Land

Ocean

Data

Knowledge

Scenario

Operational use

Monitoring and understanding of
the Earth environment change

Policy design

GCOM 1st Generation
GCOM-W1

Orbit

GCOM-C1Type : Sun-synchronous, subrecurrent
Altitude : 699.6 km
Inclination : 98.19 degrees
Local time of ascending node :13:30

Type : Sun-synchronous, subrecurrent
Altitude : 798 km
Inclination : 98.6 degrees
Local time of ascending node :10:30

Satellite
overview
Mission life

5 years

Launch vehicle

H2A launch vehicle

Mass
Instrument
Launch(target)1940kg(AMSR2 404 kg)2020 kg(SGLI 480 kg included)

AMSR 2

Second Generation Global
Imager (SGLI)January 2012

January 2014 (TBD)

GCOM geophysical products
Most of the parameters are compatible with GCOS requirement

・Vegetation indices
・Soil moisture・Biomass burning
・Land surface temp.

・Aerosol properties
・Cloud properties
・Precipitable water・Precipitation

・Snow impurity
・Snow grain size
・Sea ice extent
・Snow depth/extent

・Sea surface temperature
・Sea surface wind speed
・Chlorophyll-a・Suspended solid

GCOM observation targets
Radiation budget

Carbon cycle

Cloud/aerosolchanges andRatiative forcing
Ice-albedo
feedback

Major unknown factor
in climate modelingSoilmoisture

Cloudproperties Cloud
Aerosolliquidproperties
water
Water
vapor

Carbon cycle andvegetation
productionLand
surfaceTemp. Vegetation
Land
cover

production

Snow
Abovesurface
groundSeabiomass
Snow properties
surfacedepth
Snow Temp.
Snow IcePAR
surface
distribution
Sea
Temp.
Sea icesurfaceconcentration
wind
Ocean
color
Precipitation

Water and energy
cycle in global scale

Direct effect tohuman activities(severe weather, flood,water resources)

Water cycle changes
by global warming

Water/Energy cycle

Carbon and heatpool and coastal
environment

Sink and pool of CO2(major greenhouse gas)

GCOM-W1 Satellite Targets of GCOM-W are water-energy cycle, and will carry
the Advanced Microwave Scanning Radiometer-2 (AMSR2).
 AMSR2 will continue AMSR-E observations (water vapor,cloud liquid water, precipitation, SST, sea surface wind speed,sea ice concentration, snow depth, and soil moisture).

Orbit

Sun synchronous orbit
Altitude: 699.6km (on Equator)
Inclination: 98.186°
Local sun time: 13:30±15minLife

5 years

LaunchJanuary 2012 by H-IIA Rocket

Satellite
scale

5.1m (X) X 17.6m (Y) X 5.0m (Z)(on-orbit)

Satellite
mass

Total 1,940kg
Mission mass: 404kg (AMSR2)
Bus mass (dry): 1,391kg
Propellant: 144kg

Powergeneration

More than 4050W (EOL)AMSR2 SUAMSR2 CU

GCOM-W (Water)Overview of AMSR2 instrument

Deployed

Stowed Deployable main reflector systemwith 2.0m diameter.
 Frequency channel set is identicalto that of AMSR-E except 7.3GHz
channel for RFI mitigation.
 Two-point external calibration
with the improved HTS (hot-load).
 Deep-space maneuver will beconsidered to check theconsistency between mainreflector and CSM.
AMSR2 Channel Set

AMSR2 characteristics
Scan

Conical scan

Swath width

1450kmAntenna2.0m offset parabola

Digitalization

12bit

Incidence
angle

nominal 55 degree

Polarization

Vertical and Horizontal

Dynamic range 2.7-340K

Beam width[deg](Ground res.
[km])

Center
Freq.
[GHz]

Band
width
[MHz]

6.925/
7.3

350

10.65

100

18.7

200

23.8

400

36.5

1000

0.35 (7 x 12)

89.0

3000

0.15 (3 x 5)

Polar
izati
on

Samplinginterval[km]

1.8 (35 x 62)
VandH

1.2 (24 x 42)
0.65 (14 x 22)

100.75 (15 x 26)
5

Cooperation with
NPOESS and METOP
LTAN/LTDN

05 06 07 08 09 10 11

12 13 14 15 16 17 18 19 20 21 22 23 24 25
GCOM-C1

J

10:30
/
09:30

U

GCOM-C2SGLI

DMSP-F16

DMSP-F18

SSM/IS

SSM/IS

AVHRR

GCOM-C3

SGLI

SGLI

NOAA-MMODIS

Terra
METOP A

METOP BAVHRR/3

AVHRR/3

E

METOP CMETOP D

AVHRR/3

AVHRR/3+

Sentinel-3A

Sentinel-3B

OLCI, SLST

J

13:30
U

Aqua

GCOM-W1AMSR-EAMSR2NOAA-NNOAA-N’

AVHRR/3

AVHRR/3

GCOM-W2

GCOM-W3

AMSR2

AMSR2

NPOESS-C1

NPOESS-C3
MIS

MODIS

VIIRS

Aqua

NPP

VIIRS

VIIRS

E

Passive Microwave Radiometer

SensorAft- NPOESS/METOP/GCOM

SensorPre- NPOESS/METOP/GCOM

Visible/Infrared Imager

SensorAft- NPOESS/METOP/GCOM

SensorPre- NPOESS/METOP/GCOM GCOM-C/SGLI data will help fill a current gap in the NPOESS/VIIRS morning
orbit, in conjunction with AVHRR+Sentinel.
 GCOM-W/AMSR2 data will complement NPOESS/MIS global microwave
radiometry data.

Global Precipitation Measurement (GPM)
The Global Precipitation
Measurement (GPM) is afollow-on and expanded
mission of the Tropical Rainfall
Measuring Mission (TRMM)

TRMM Era

GPM EraCore Satellite

Constellation
Satellites

Core Satellite (JAXA, NASA)
Dual-frequency precipitation radar (DPR)
GPM Microwave Imager (GMI)• Precipitation with high precision• Discrimination between rain and snow
• Adjustment of data from constellation
satellites(launch in 2013)

Constellation Satellites(International Partners)
Microwave radiometers
Microwave sounders• Global precipitation every 3 hours(launch around 2013)¾ Improve the accuracy of both long-term and short-term
weather forecasts¾ Improve water resource management in river control andirrigation systems for agriculture25

Sensors aboard GPM core satellite
Flight direction

Dual-frequency precipitation
radar (DPR) consists of
Ku-band (13.6GHz) radar : KuPR
andKa-band (35.5GHz) radar : KaPR

407 km altitude,
65 deg inclination

DPR

GMI

Range resolution= 250m / 500m

KuPR (13.6 GHz)
swath width=245 km

KaPR (35.5 GHz)
swath width=120 km

Microwave radiometerswath width =800km

5km
26

Precipitation Satellite Missions
Year
Nonsunsynch
ronou
s

Early
-AM

05 06 07 08 09 10 11 12 13 14 15

1
GPM Core (65) 6

TRMM

17 18

NASA constellation (40)
GPM-Br (30)
Megha-Tropiques(20)DMSP-F19

DMSP-F13

DMSP-F20

DMSP-F17

NPOESS-C2

NOAA-MMidAM

DMSP-F16

DMSP-F18
FY-3A

FY-3C

MetOp-APM

FY-3E

MetOp-B

Aqua

MetOp-C

GCOM-W1
FY-3B

NOAA-N

GCOM-W2
FY-3D

NOAA-N'

NPOESS-C1

FY-3F
-C3

NPP
Radar + MWR

MWR

MWS
27

Current and future conical scanningmicrowave radiometers
SensorSSM/I

TMI

AMSR-E

SSMIS

Windsat

AMSR2

GMI

MIS

Satellite

DMSP
series(~F15)

TRMM

Aqua

DMSP
series(F16~)

Coriolis

GCOM
W1

GPM

NPOESS
C2

Provider

DoD,
U.S.

NASA

JAXA

DoD,
U.S.

NASA

JAXA

NASA

NOAA/
NRL

Freq.
[GHz]

19.35 85.5

10.65 –
85.5

6.93 –
89.0

19.35 –
183.3

6.8 –
37.0

6.93 –
89.0

10.65 –
183.3

6 - TBD

Antennasize [m]

0.61

0.61

1.6

0.61

1.8

2.0

1.2

1.8

28

Satellite data access to TRMM/AMSR-E
data via www/ftp site

TRMM Web site:
http://www.eorc.jaxa.jp/TRMM/index_e.htm
AMSR-E Web site:
http://sharaku.eorc.jaxa.jp/AMSR/index_e.htm

29Satellite Product Categories
Standard ProductOperational product that produced

Subset ProductSub-set of specific region or area, produced from standard
product.

Research ProductProduct that uses research algorithm and is produced mainly
for research objectives. Products are not produced
operationally.

Near Real-time ProductProduct that is released in near real-time in order to meetrequirement for operational uses, such as numerical weather
prediction. Products are released within 3-to-4 hours aftersatellite observation to operational agencies, but geolocation
accuracy could be worse than standard product in several
kilometers.
30

Data Processing for Standard Products(TRMM/PR as example)
Raw
Rawdatadata

Transferred from TRMM

Level
Level00

Quality checked raw data

Level
Level11

Calibrated data (PR received
power etc.)

Level
Level22

Geophysical parameters (rain rate,
etc.)

Level
Level33

Temporary and spatiallyaveraged (monthly mean rainfall)

InstantaneousMonthly, or 3-hourly

31

JAXA/EORC Tropical Cyclone Database
Browse images, 3D movies
and data of tropical
cyclones observed by
TRMM, AMSR-E, AMSR are
available.
Updated 1-1.5 months
after observation.

http://sharaku.eorc.jaxa.jp/TYP_DB/index_e.shtml
32

Example of Tropical Cyclone Database
TRMM/PR 3D Observation of thecyclone attack in Myanmar
Date/Time:Apr 28,2008 18:33(UTC)
Satellite/Sensor:TRMM/PR,VIRS
Lat/Lon:8.20N-18.84N, 79.66E-90.30E

Track Chart and Observation Area

33

TRMM Tropical Cyclone Real-Time
Monitoring
Global regions (Asia,
Americas, Oceania)
Operating in near-realtime (3-6 hours after
observation)
Browse images of PR, TMI
and storm tracks areavailable

34

TRMM Near Real Time Images
Operational from Feb.
2008
Visualize TRMM near-realtime data 3-6 hours after
observation
Easy browsing, easy
selection of dates andparameters. Displaying
user selected regions withzoom-up and down
Overlaying multiparameters.
Displaying by Google Earth.

http://sharaku.eorc.jaxa.jp/trmm/RT/index_e.html
35

TRMM Near Real Time Images (cont.)
Data are averaged in 0.05-degree
lat/lon grid for each orbit
Standard products

PR surface rainfall
PR 20-layer rain rate (2,4,6,8,10km)
PR storm height
PR rain type
TMI surface rainfall
TMI brightness temperature for
85GHz-V/H
TMI total cloud water content
TMI total cloud ice content
VIRS IR channel

Reference images

TMI-PR differences
PR(rain) +TMI(rain)+VIRS(cloud)PR(rain)+VIRS(cloud)Research products

will be added. (e.g., TMI SST, VIRS
SST, etc.)36

Global Rainfall Map in Near Real Time
Displaying global rainfall
map merging TRMM.
AMSR-E and othersatellite information
Available 4-hr after
observation
Browse images, 24-hr
animation, displaying by
Google Earth
0.1-degree lat/lon grid,hourly products
Data are also available
via password protected
ftp site
Based on JST/CREST
GSMaP algorithm

http://sharaku.eorc.jaxa.jp/GSMaP/
37

Global Satellite Mapping of Precipitation(GSMaP)
GSMaP team (Osaka-prefecture Univ., Osaka Univ., MRI, JAXA, NICT,
more) funded by JST/CREST. Details will be presented by Dr. Ushio.
Production of high precision and high resolution global precipitation
map by using satellite-borne microwave radiometers.
Retrievals from microwave radiometers (TMI, AMSR-E, AMSR, SSM/Ix3)
Utilization of precipitation radar, GEO’s visible and IR radiometers

Combination of the moving vector (Joyce et al., 2004) and Kalmanfiltering method
The moving vector method was used in CMORPH
Advantage: MWR based approach (not Tb but cloud motion), Fast processing
time
Disadvantage: Not include the developing
and decaying process of precipitation

New Kalman filter approach
Refine precipitation rate on Kalman gain after
propagating the rain pixel
The Kalman gain is determined from thedatabase on the relationship between the
IR Tb and surface rain rate.

Real time version is developed by JAXA.

38

Flowchart of GSMaP algorithm
GEO IR data
Zonal

Present IR

Cloud motion vectors

1-hr before IR

Past PMW data
1-hr before PMW

Meridional

PMW interpolatedby the motion
vectors

Kalman filter
Present MWR data

(Outsides PMW overpasses)

MWR data observed during present 1-hr
(PMW overpasses)(Ushio et al., 2008)

Present
Rain map
39

Processing Flow of GSMaP_NRT
Observation data by microwave radiometer (MWR) and Geostationary satellite
available within three hours from observation are utilized in the GSMaP_NRT
system .
These data are collected and processed at the system, and output products(rainfall) are delivered to users within four hours after observation.

40

GSMaP_NRT observed cyclone attack in
Myanmar (May 2008)00Z
May 3,
2008

April 28～May 3

41

Animation of every 6 hours

April 28～May 4, 200842

Latent Heat Research Product
Latent heating profile
estimated by PR 3-D
observation
All data are available
during PR observation
period (updating every 1month)
Base on Spectral Latent
Heating (SLH) algorithm(Shige et al., 2004, 2007,
2008)
Orbital (non-grid, grid)and monthly data in 0.5deree lat/lon grid areavailable

http://www.eorc.jaxa.jp/TRMM/lh/index.html
43

Atmospheric Circulation Dynamics
Tropical rainfall and atmospheric circulation
Atmospheric
Atmosphericcirculation
circulation
ofof normal
normalyears
years

Atmospheric
Atmosphericcirculation
circulationofof
ElElNiño
Niñoyears
years

Tropical rainfall: Primary “heat” engine for theatmospheric circulation on the Earth. Its fixed
quantitative observations are essential to theresearch on climate change and abnormal weather.
44

Latent heat observed by TRMM
(a) Latent heat at altitude of 7.5km(b) Latent heat at altitude of 2km（Shige, Takayabu, et al., 2004, 2007, 2008)

Latent heat distribution during December, January, and February using
TRMM PR 3D data between 1998 and 2007. The data can be utilized forevaluation of global water & energy cycle and for improvement of climate
models.
http://www.eorc.jaxa.jp/TRMM/lh/index.html

45

AMSR-E Level 3 Browse Images
 Displaying browse
images of AMSR-E
Level 3 (daily)
products
 brightness
temperatures
 water vapor
 cloud liquid water precipitation
 sea surface wind sea surface
temperature sea ice concentration
 snow waterequivalence soil moisture
http://sharaku.eorc.jaxa.jp/cgi-bin/adeos2/amsr/l3brws/l3brws_top.cgi?lang=e

El Niño Watch
 Daily monitoring of sea
surface temperature(SST) by AMSR-E and
its anomaly fromclimatology (30-yr
average) over thetropical Pacific region
 Images have archived
since June 2002.

http://sharaku.eorc.jaxa.jp/cgi-bin/amsr/elni2/elni2.cgi?lang=eArctic Sea-Ice Monitor by AMSR-E
 Daily monitoring ofArctic sea-ice
concentration by
AMSR-E Images have archived
since June 2002.
 Collaboration with
International Arctic
Research Center(IARC)

http://www.ijis.iarc.uaf.edu/cgi-bin/seaice-monitor.cgi?lang=eChanges in AMSR-E sea ice extent

Previous satellite minimum
Satellite record minimum
AMSR-E sea ice extent over northern polar region around September 20 of recent 6 years (2002-2007).
Images are available at the Arctic Sea-Ice Monitor site maintained by the International Arctic ResearchCenter (http://www.ijis.iarc.uaf.edu/en/index.htm).

Recent status of ice extent
Minimum ice extent of 2008: 470.8 km2 (recorded at 9 Sep. 2008)

2008
2007

Time series of AMSR-E sea ice extent over Arctic Oceans. Daily updates are available at the
Arctic Sea-Ice Monitor site maintained by the International Arctic Research Center(http://www.ijis.iarc.uaf.edu/en/index.htm).

JAXA activities for hydrologicalapplications

51

Rainfall Measurement and our life
Rain affects most everyone's life &work;
Food production; andFlood and drought.

Rain is a key variable in;Weather prediction models;
Climate models; andAir-sea interaction models, etc.

Rain is one of hardest meteorologicalparameters to measure;because of its spatial and temporalvariability;
contribution by rainfall measuring satellites
is highly expected; andAppearance of TRMM satellite52Collaboration in Global Flood Alert System(http://www.internationalfloodnetwork.org)
GPM

JAXA are collaborated with
IFNet and UNESCO/ICHARM
for improving satellite
rainfall estimation for
utilization in flood warning. Raw Data

On-Line(Global Precipitation
Measurement)

Precipitation Information
around the Upstream

Ground Stations(NASA, JAXA)

Data Processing System (NASA, JAXA)
Real-time 3-hourly Precipitation Data

On-LineEstimation of
precipitation probability
Present
Precipitation>

Estimated
Precipitation
Probability

Flood
Alert !

E-mailDisaster Prevention
Organizations of
the concerned countries

53Collaboration with Univ. of Tokyo
Prof. Oki’s group (Univ. of Tokyo) and JAXA are developing
Global Land Data Assimilation System, which can provide
information of river discharge
System flowchart of Prof. Oki’s group
Today’s Earth

Yesterday’s Earth

Weather Forecast

Satellite Precipitation Map
Precipitation

Surface Variables
Conversion (resolution, variables)

Leaves (LAI/Al)
Et, REt

Iso-MATSIRO Schematic

Rl
h, Ra

Ec, REc

P, RP

Et, REt
Transport

C, RC
Vl, RlForcing Data

Ss, RSszl

Snow

Eb, REb

Temporal variation

Os, ROs
Infiltration

Land Surface Model (Iso-MATSIRO)

θw, Rθw
Soil ice

Back
Diffusion

Xylem
Vegetation Component

Sn, RSn
Snow Water

RunoffInitial results:
River Discharge
Daily time series, average
all over Japan

Soil Water

Transport term Diffusion termdV l ∂ R l∂ 2 Rl ⎞1 ⎛⎜ E t A l ∂ R l
⎟
=
− Dτ
2
ρ ⎝⎜ LAI ∂ z l
dz l ∂ t∂ z l ⎠⎟Ob, ROb

Hydrological Variables

River Routing Model (TRIP, J-TRIP)
River Discharge

54

Results of Today's Earth

Left: AMSR-E soil moistureobservation at 1 Jul. 2008 (by Dr.
Fujii, JAXA)
Right: 5cm Soil moisture model
results by Today's Earth55

What is Sentinel Asia?
Sentinel Asia is a "voluntary andbest-efforts-basis initiatives" ledby the APRSAF (Asia-PacificRegional Space Agency Forum)
to share disaster information in
the Asia-Pacific region on the
Digital Asia (Web-GIS) platform
and to make the best use ofearth observation satellites datafor disaster management in the
Asia-Pacific region.
Sentinel Asia is to initially be aninternet-based, node-distributed,
information distribution
backbone, eventuallydistributing relevant satellite and http://dmss.tksc.jaxa.jp/sentinel/
in-situ spatial information on
multiple hazards in the AsiaPacific region.

56

Overview of Sentinel Asia

57

Sentinel Asia For Environment (SAFE)(expansion of Sentinel Asia)
Land UseMonitoring

SAFE Portal

Prototype System
for Country A

training
Dataset Distribution

Users

software

Terra/
MODIS

Prototype System
for Country B

MTSAT-1RPrototype System
for Country C
Prototype Systemfor country A

Dataset Distribution

TRMM/PR
Aqua/
AMSR-E

Water Resources
Monitoring

Value-added
Information
and Data

training

Users

softwarePrototype System
for Country B
Prototype System
for Country C

58

Appendix

59

TRMM Online data (1)Standard Product

PR/TMI/VIRS Data

Earth Onservation Data and Information System - JAXA/EORC
https://www.eoc.jaxa.jp/iss/jsp/indexEn.html
Need registration

DAAC (Goddard Distributed Active Archive Center) - NASA/GSFC
http://lake.nascom.nasa.gov/data/dataset/TRMM/index.html

LIS (Home Page/download data) - NASA/MSFC
http://thunder.msfc.nasa.gov/

Subset Product

Level 3 Selected Monthly Rainfall Data Set - JAXA/EORC

http://www.eorc.nasda.go.jp/TRMM/imgdt/L3_data/index.htm

TRMM Specific Area Data Set - JAXA/EORC

http://www.eorc.jaxa.jp/TRMM/data/monitoring/TSD/gisp_e.htm
PR subset for Japan and GAME Area (1997.12-2000.9, 2004.4-2005.2)

TRMM Gridded Rainfall Products (text) - NASA/GSFC/TSDIS
ftp://trmmopen.gsfc.nasa.gov/pub/

60

TRMM Online data (2)Near Real-time Product (Level 1 & 2)

TRMM Realtime System - NASA/GSFC

http://tsdis.gsfc.nasa.gov/trmmrt/index.htm
Need registration

Near Real-time rain map (research)

3B40RT (HQ MW only), 3B41RT (Geo-stationary IR data), 3B42RT(MWR+IR) - NASA/GSFC
Online data available within 10 hours
http://trmm.gsfc.nasa.gov/

GSMaP - JAXA/JST

http://sharaku.eorc.jaxa.jp/GSMaP/
Online data available within 4 hours
Need registration for NRT use
Past data are available from JST/CREST GSMaP web site:
http://www.radar.aero.osakafu-u.ac.jp/~gsmap/index_english.html

Research Product

VIRS Sea Surface Temperature - JAXA/EORC

http://www.eorc.jaxa.jp/TRMM/data/monitoring/day_vrs/index_e.htm

TMI Sea Surface Temperature - JAXA/EORC
Under construction (coming soon)

61