# Presentation: 678004

## The Joint Center  For 

**The Joint Center  For **
**Satellite Data Assimilation (JCSDA) – **
**Helping to improve Climate, Weather, Ocean and Air Quality Forecasts  and**
**Improve The Quality of Climate Data Sets**

- John Le Marshall
- Director, JCSDA

**Notes:**
Note prep for 4d-cts data, rad ass for EnKF etc.

##   Australian Federal Departmental

-     Excellence Award
-   SPIE Award for Scientific
-     Achievement in Remote Sensing
-   NASA Exceptional Scientific  Achievement Medal
- Current/Recent
-  Co-Chair – International TOVS
-    Working Group
-  Adjunct Professor University of
-    New South Wales –Mathematics
-  Adjunct Professor La Trobe
-    University –Physics
-  Adjunct Professor University of
-    Wisconsin –Meteorology and
-    Oceanography

**Dr. John F. Le Marshall**
**Director, JCSDA**
** ****NASA, NOAA, DoD Joint Center**
**for Satellite Data Assimilation**** **

**The Joint Center  For Satellite Data Assimilation (JCSDA) – ****Helping to improve Climate, Weather, Ocean and Air Quality ****Forecasts  and Improve The Quality of Climate Data Sets **

## Overview

**JCSDA – Background**
**The Satellite Program – The Challenge**
**JCSDA**
**NPOESS**
**Preparation for NPOESS using Heritage Instruments**
**Plans/Future Prospects**
**Summary **

## Background

**The value of satellite Observations**

## Slide 5

**Notes:**
Begin with 500 mb day 5 anomaly correction from NCEP global forecast system. If the coeff. is greater than 60%, we can say the model can predict the synoptic scale features with a reasonable skill.
Overall, we see a steady growth of NWP prediction skill in last few decades. This growth is due to 1) the advance in model physics, 2) an increasing computing power that allows us run high resolution models, and 3) using more and more satellite data.
After 1998, you see a sudden jump in the skills both in N/S hemisphere. This is mainly attributed to the first assimilation of AMSU. In SH, there is not a lot of conventional data, satellite measurements have filled in the data void areas. 
Today, we have seen the ACs in SH and NH are approaching to the same level. 

## Data Assimilation Impacts in the NCEP GDAS

- AMSU and “All Conventional” data provide nearly the same amount of improvement to the Northern Hemisphere.

## Anomaly correlation for days 0 to 7 for 500 hPa geopotential height in the zonal band 20°-80° South for January/February. The red and blue arrows indicate use of satellite data in the forecast model  has doubled the length of a useful forecast ( ie a forecast with AC=0.6).

**Anomaly correlation for days 0 to 7 for 500 hPa geopotential height in the zonal band 20°-80° South for January/February.**** ****The red and blue arrows indicate use of satellite data in the forecast model  has doubled the length of a useful forecast ( ie a forecast with AC=0.6).**

## The Challenge

**5-Order Magnitude Increase in Satellite Data ****Over 10 Years**

## The Challenge

- Satellite Systems/Global Measurements

**Aqua**

**Terra**

**TRMM**

**SORCE**

**SeaWiFS**

**Aura**

**Meteor/**
**SAGE**

**GRACE**

**ICESat**

**Cloudsat**

**Jason**

**CALIPSO**

**GIFTS**

**TOPEX**

**Landsat**

**NOAA/POES**

**GOES-R**

**WindSAT**

**NPP**

**COSMIC/GPS**

**SSMIS**

**NPOESS**

**MSG**

##  5-Order Magnitude Increase in satellite Data Over 10 Years

- Count (Millions)

- Daily Upper Air Observation Count

- Year

- Satellite Instruments  by Platform

- Count

**NPOESS**
**METEOP**
**NOAA**
**Windsat**
**GOES**
**DMSP**

- 1990

- 2010

- Year

**Notes:**
Currently, we use ~105 observations per day in operational models out of total of more than 106.  There are estimated to be ~1011 observations per day by 2010.  This estimate does NOT include GIFTS or Doppler Wind Lidar.   This volume of data is outside our current capacity and design bracket for most data assimilation systems. 

Last column represents using 10 percent of the data volume (note logarithmic scale).  That is what feasible for scientific and logistical reasons.

Note that the ordinate in the left panel has a logarithmic scale.

## JCSDA Instrument Database – June 2006

## Slide 12

## Slide 13

## The JCSDA

**History, Mission, Vision, ....**

## History

-  In 2001 the Joint Center was established2 by NASA and NOAA and in 2002, the JCSDA expanded its partnerships to include the U.S. Navy and Air Force weather agencies.
*2 *_* Joint Center for Satellite Data Assimilation: Luis Uccellini, Franco Einaudi, James F. W. Purdom, David Rogers: April 2000.*_

## JCSDA Partners

- Pending

## JCSDA Mission and Vision

**Mission: Accelerate and improve the quantitative use of research and operational satellite data in weather. ocean, climate and environmental analysis and prediction models**
**Vision: A weather, ocean, climate and environmental  analysis and prediction community empowered to effectively assimilate increasing amounts of advanced satellite observations and to effectively use the integrated observations of the GEOSS **

## JCSDA SCIENCE PRIORITIES 

**Science Priority I - Improve Radiative Transfer Models **
***       ******- Atmospheric Radiative Transfer Modeling – The Community Radiative Transfer Model (CRTM)***
***      ******-  Surface Emissivity Modeling***
**Science Priority II - Prepare for Advanced Operational Instruments**
**Science Priority III -Assimilating Observations of Clouds and Precipitation    **
***      ******- Assimilation of Precipitation***** **
**      ****- *****Direct Assimilation of Radiances in Cloudy and Precipitation Conditions***
**Science Priority IV - Assimilation of Land Surface Observations from Satellites **
**Science Priority V - Assimilation of Satellite Oceanic Observations **
**Science Priority VI – Assimilation for air quality forecasts**

- Strat. Plan/JTOPS

## Goals – Short/Medium  Term

**Increase uses of current and future satellite data in Numerical Weather and Climate Analysis and Prediction models**
**Develop the hardware/software systems needed to assimilate data from the advanced satellite sensors**
**Advance common NWP models and data assimilation infrastructure**
**Develop a common fast radiative transfer system(CRTM)**
**Assess impacts of data from advanced satellite sensors on weather and climate analysis and forecasts (OSEs,OSSEs)**
**Reduce the average time for operational implementations of new satellite technology from two years to one**

**Notes:**
Point 2 AIRS, IASI, CRIS, GIFTS, GOES R

## Major Accomplishments

**Common assimilation infrastructure at NOAA and NASA **
**Community radiative transfer model**
**Common NOAA/NASA land data assimilation system**
**Interfaces between JCSDA models and external researchers**
**Snow/sea ice emissivity model – permits 300% increase in sounding data usage over high latitudes – improved polar forecasts**
**MODIS winds, polar regions, - improved forecasts - Implemented**
**AIRS radiances assimilated – improved forecasts - Implemented**
**Improved physically based SST analysis -  Implemented**
**Preparation for advanced satellite data such as METOP (IASI,AMSU,MHS...),   , NPP (CrIS, ATMS....), NPOESS, GOES-R data underway.**
**Advanced satellite data systems such as DMSP (SSMIS), CHAMP GPS, COSMIC GPS, WindSat tested for implementation.**
**Impact studies of POES AMSU, HIRS, EOS AIRS/MODIS, DMSP SSMIS, WindSat, CHAMP GPS on NWP through EMC parallel experiments active**
**Data denial experiments completed for major data base components in support of system optimisation **
**OSSE studies completed **
**Strategic plans of all Partners include 4D-VAR  **

## Satellite Data used in NWP

**HIRS sounder radiances**
**AMSU-A sounder radiances**
**AMSU-B sounder radiances**
**GOES sounder radiances**
**GOES, Meteosat, GMS winds**
**GOES precipitation rate**
**SSM/I precipitation rates**
**TRMM precipitation rates**
**SSM/I ocean surface wind speeds**
**ERS-2 ocean surface wind vectors**

**Quikscat ocean surface wind vectors**
**AVHRR SST**
**AVHRR vegetation fraction**
**AVHRR surface type**
**Multi-satellite snow cover**
**Multi-satellite sea ice**
**SBUV/2 ozone profile and total ozone**
**Altimeter sea level observations (ocean data assimilation)**
**AIRS**
**MODIS Winds**
**...**

- ~31 instruments

## Sounding data used operationally within the 

**Sounding data used operationally within the **
**GMAO/NCEP   Global Forecast System**

| AIRS

HIRS sounder radiances

AMSU-A sounder radiances

MSU
AMSU-B sounder radiances

GOES sounder radiances

SBUV/2 ozone profile and total ozone | On

14 -  on
15 -  off
16 -  off
17 -  on

15 -  on
16 -  on
17 -  off
18 -  on
AQUA
14 -  on
15 -  on
16 -  on
17 -  on

10 -  on
12 -  on

16 -  on
17 -  on |
| --- | --- |

##  Some Satellite Data in the Process of Being 

** ****Some Satellite Data in the Process of Being **
**Transitioned into Operations**** **

| Satellite/Instrument | Analysis
 | Comments |
| --- | --- | --- |
| COSMIC | GSI  NOGAPS | Currently RT cycling in GSI |
| CHAMP | GSI  NOGAPS | NRTassim. tests completed, awaiting RT data access |
| WINDSAT | SSI GSI NOGAPS | Initial SSI trial,positive impact. RT trial current |
| SSMIS | GSI   NOGAPS | Non real time testing, positive impact. |
| MODIS υ.2 (EE) | GSI | EE implemented for intelligent thinning of AMVs |
| AIRS υ.2 (every fov -251 channels used) | SSI GSI NOGAPS | SSI testing complete, GSI testing complete. |
| AURA OMI | GSI | Total ozone successfully assimilated, still testing |
| AMSRE(E) | GSI | Currently cycling in GSI, impacts positive. |
| NOAA 18  MHS | GSI | Transitioned for next operational analysis |
| GFO | RTOFS | Assim. tests current. |

## NPOESS

**The Instruments**
**Applications **
**    ****-NWP/Environmental Analysis and Prediction**
**    ****-Ocean Analysis and Prediction**
**    ****-Climate – Short/Long Term Prediction, Reanalyses         (tuning, calibration)**
**Preparations for NPOESS– The CRTM**
**Preparations for NPOESS- Assimilating Data from Heritage Instruments**

## NPOESS

- The Instruments
- Applications
-     -NWP/Environmental Analysis and Prediction
-     -Ocean Analysis and Prediction
-     -Climate – Short/Long Term Prediction, Reanalyses         (tuning, calibration)

## NPOESS Instrument Summary-2005

| Acronym | Acronym Expansion |
| --- | --- |
| APS | Aerosol Polarimetric Sensor |
| ATMS | Advanced Technology Microwave Sounder  |
| CrIS | Cross Track Infrared Sounder |
| A-DCS | Advanced Data Collection System |
| ERBS | Earth Radiation Budget Sensor |
| GPSOS | Global Positioning System Occultation Sensor |
| OMPS | Ozone Mapping and Profiler Suite |
| ALT | Radar Altimeter |
| SARSAT | Search & Rescue Satellite Aided Tracking |
| SESS | Space Environment Sensor Suite  |
| TSIS | Total Solar Irradiance Sensor |
| VIIRS | Visible/Infrared Imager/Radiometer Suite |
| CMIS | Conical Microwave Imager/Sounder |

## NPOESS Instrument Summary-2006

| Acronym | Acronym Expansion |
| --- | --- |
| APS | Aerosol Polarimetric Sensor |
| ATMS | Advanced Technology Microwave Sounder  |
| CrIS | Cross Track Infrared Sounders |
| A-DCS | Advanced Data Collection System |
| ERBS | Earth Radiation Budget Sensor |
| GPSOS | Global Positioning System Occultation Sensor |
| OMPS (nadir ) | Ozone Mapping and Profiler Suite |
| ALT | Radar Altimeter |
| SARSAT | Search & Rescue Satellite Aided Tracking |
| SESS | Space Environment Sensor Suite  |
| TSIS | Total Solar Irradiance Sensor |
| VIIRS | Visible/Infrared Imager/Radiometer Suite |
| CMIS | Conical Microwave Imager/Sounder |

## NPOESS provides key climate information on :

| Atmosphere | Temperature |
| --- | --- |
|  | Moisture |
|  | Winds (polar) |
|  | Clouds |
|  | Aerosols |
|  | ERB |
|  | Precipitation |
|  | Ozone |
|  | Trace gases |
| Ocean | Surface temperature and emissivity |
|  | Surface winds |
|  | Ice Cover |
|  | Ocean Colour |
|  | Sea level |
| Land | Skin temperature |
|  | Vegetation |
|  | Snow /Ice cover |
|  | Albedo |

## NPOESS provides key environmental analysis and prediction information on :

| Atmosphere | Temperature |
| --- | --- |
|  | Moisture |
|  | Winds (polar) |
|  | Clouds |
|  | Aerosols |
|  | ERB |
|  | Precipitation |
|  | Ozone |
|  | Trace gases |
| Ocean | Surface temperature and emissivity |
|  | Surface winds |
|  | Ice Cover |
|  | Ocean Colour |
|  | Sea level |
| Land | Skin temperature |
|  | Vegetation |
|  | Snow cover |
|  | Albedo |

## NPOESS

- Applications
-     -NWP/Environmental Analysis and Prediction
-     -Ocean Analysis and Prediction
-     -Climate – Short/Long Term Prediction, Reanalyses         (tuning, calibration)
- _Note_
- Multi-Variate Multi-Instrument Analysis
- (Radiance) Observations Tuned/Cross Calibrated
-     -Differences due to Radiative Transfer Error , Model Error (bias), Observation Error, Calibration Error,  ....
- Modern Analysis Methods Aid Use Of Asynoptic Observations

## JCSDA NPOESSPreparation

- Preparations for NPOESS– The CRTM
- Preparations for NPOESS- Assimilating Data from Heritage Instruments

## PREPARATION FOR NPOESS:Development and Implementation of the Community Radiative Transfer Model (CRTM)

*P. van Delst, Q. Liu,* *F. Weng,* *Y. Chen*, *D. Groff, B. Yan, N. Nalli, *
- R. Treadon, J. Derber and *Y. Han  .....*

## Community Contributions

**Community Research:  Radiative Transfer science**
- AER. Inc: Optimal Spectral Sampling (OSS) Method
- NRL – Improving Microwave Emissivity Model (MEM) in deserts
- NOAA/ETL – Fully polarmetric surface models and microwave radiative transfer model
- UCLA – Delta 4 stream vector radiative transfer model
- UMBC – aerosol scattering
- UWisc – Successive Order of Iteration
- CIRA/CU – SHDOMPPDA
- UMBC SARTA
- Princeton Univ – snow emissivity model improvement
- NESDIS/ORA – Snow, sea ice, microwave land emissivity models, vector discrete ordinate radiative transfer (VDISORT), advanced double/adding (ADA), ocean polarimetric,  scattering models for all wavelengths
**Core team (JCSDA - STAR/EMC): Smooth transition from research to operations**
- Maintenance of CRTM (OPTRAN/OSS coeffs., Emissivity upgrade)
- CRTM interface
- Benchmark tests for model selection
- Integration of new science into CRTM

## CRTM has been integrated into the GSI at NCEP/EMC 

Beta version CRTM has been released to the public

CRTM with OSS (Optimal Spectral Sampling) has been preliminarily implemented and is being evaluated and improved.

**CRTM has been integrated into the GSI at NCEP/EMC **
**Beta version CRTM has been released to the public**
**CRTM with OSS (Optimal Spectral Sampling) has been preliminarily implemented and is being evaluated and improved.**

## COMMUNITY RADIATIVE TRANSFER  MODEL 

**COMMUNITY RADIATIVE TRANSFER  MODEL **
**CRTM**

- Below are some of the instruments for which we currently have transmittance coefficients.
- abi_gr (gr == GOES-R) airs_aqua amsre_aqua amsua_aqua amsua_n15 amsua_n16 amsua_n17 amsua_n18 amsub_n15 amsub_n16 amsub_n17 avhrr2_n10 avhrr2_n11 avhrr2_n12 avhrr2_n14 avhrr3_n15 avhrr3_n16 avhrr3_n17 avhrr3_n18 hirs2_n10 hirs2_n11 hirs2_n12 hirs2_n14 hirs3_n15 hirs3_n16 hirs3_n17 hirs3_n18 hsb_aqua imgr_g08 imgr_g09 imgr_g10 imgr_g11 imgr_g12 mhs_n18 modisD01_aqua (D01 == detector 1, D02 == detector 2, etc) modisD01_terra modisD02_aqua modisD02_terra modisD03_aqua modisD03_terra modisD04_aqua modisD04_terra modisD05_aqua modisD05_terra modisD06_aqua modisD06_terra modisD07_aqua modisD07_terra modisD08_aqua modisD08_terra modisD09_aqua modisD09_terra modisD10_aqua modisD10_terra modis_aqua (detector average) modis_terra (detector average) msu_n14 sndr_g08 sndr_g09 sndr_g10 sndr_g11 sndr_g12 ssmi_f13 ssmi_f14 ssmi_f15 ssmis_f16 ssmt2_f14 vissrDetA_gms5 windsat_coriolis.....

## Preparation for NPOESS Using

**               ****Heritage Instruments**
**               ****Some Recent Advances**

## NPOESS/JCSDA 

**The NPOESS Instruments :**
**CrIS**
**ATMS**
**VIIRS**
**OMPS**
**CMIS**
**GPSOS-demanifest**

**The Heritage Instruments:**
**AIRS, IASI**
**MSU, AMSU, HSB, MHS**
**AVHRR, MODIS**
**TOMS, SBUV**
**SSMI, SSMIS, WINDSAT**
**CHAMP, SAC-C, COSMIC**

## NPOESS Instruments – CrIS, ATMS (CrIMSS)

**AMS 2006 - Future National Operational Environmental Satellites Symposium**

**Risk Reduction for NPOESS Using Heritage Sensors    ****      **

**CrIMSS will operationally produce high vertical resolution profile measurements of temperature, water vapor, and pressure.**
**     ****CrIMSS Mission Products**
**   ****Primary****	****Temperature profiles**
**	****Moisture profiles**
**	****Pressure profiles**
**	****Calibrated radiances**
**	****surface temperature**
**Secondary****   Total ozone**
**	****Sea Cloud top parameters**
**	****Precipitable water**
**	****ERB products**
**In addition to providing operational temperature, moisture, and pressure profiles, CrIMSS has the potential to provide other surface and atmo- spheric science data, including total ozone and sea surface temperature.**

**NPOESS Instruments – CrIS, ATMS (CrIMSS)**

**CrIS Instrument  Characteristics**
**Spectral Range****	**
- LWIR Band	 650-1095 cm-1
- MWIR Band	1210-1750 cm-1
- SWIR Band	 2155-2550 cm-1
**Spectral Resolution**
- LWIR Band	<0.625cm-1
- MWIR Band	<1.25cm-1
- SWIR Band	<2.50cm-1
**Registration**
- Band-to-Band Co-registration    <1.4%
- FOV Jitter	71 urad/axis
- Mapping accuracy	<1.5 km
**Field-of-Regard (FOR)**
- # of FOV		3X3
- FOV Diameter (round) 	14 km
- FOV shape match 	           0.014 degrees
**Stability**
- Radiometric <.45%LW,<.58%MW,<.77%SW
- Spectral:  ILS 	 <1% of FWHM
- Spectral shift errors	 <5 ppm
**Mass****	**** **152 kg
**Power****	**** **123 W
**Data rate****	**** **1.5 mbps
**Size****	**** **898x946x716 mm

**ATMS Instrument**

**Cross-track Infrared and Advanced Technology Microwave Sounders**

**±50° ****Cross track Scans**

**ATMS Instrument Characteristics**

## Using AIRS data in

**Preparation for the**
**               ****Cross-track Infrared Sounder**

## AIRS Data Assimilation

**     ****1-31 January 2004**
**     ****Used operational Global Forecast System (GFS)**
**	****    ****as Control**
**     ****Used Enhanced Operational GFS system Plus AIRS**
**          ****	****    ****as Experimental System**
** **

**AIRS Data Assimilation**
**J. Le Marshall, J. Jung, J. Derber, R. Treadon, **
**S.J. Lord, ****M. Goldberg, W. Wolf and H-S Liu and J. Joiner**

##  Satellite data used operationally

** ****Satellite data used operationally**
**within the NCEP Global Forecast System**

| 
 HIRS sounder radiances
 AMSU-A sounder radiances        
 AMSU-B sounder radiances
 GOES sounder radiances
 GOES 9,10,12, Meteosat
           atmospheric motion vectors
 GOES precipitation rate
 SSM/I ocean surface wind speeds
 SSM/I precipitation rates   | 
TRMM precipitation rates 
ERS-2 ocean surface wind vectors
QuikScat ocean surface wind vectors
AVHRR SST
AVHRR vegetation fraction
AVHRR surface type
Multi-satellite snow cover
Multi-satellite sea ice
SBUV/2 ozone profile and total ozone |
| --- | --- |

##  AIRS Data Usage per Six Hourly Analysis Cycle

** ****AIRS Data Usage per Six Hourly Analysis Cycle**

| Data Category    | Number of AIRS Channels    |
| --- | --- |
|    Total Data Input to Analysis
   Data Selected for Possible Use
   Data Used in 3D VAR Analysis
            (Clear Radiances) |    ~200x106  radiances (channels) 
   ~2.1x106    radiances (channels)
   ~0.85x106 radiances (channels) |

##  500hPa Z Anomaly Correlations for the GFS

** ****500hPa Z Anomaly Correlations for the GFS**
** ****with (Ops.+AIRS) and without (Ops.) AIRS Data**
**Northern Hemisphere, January 2004**

## 500hPa Anomaly Correlations for the GFS

**500hPa Anomaly Correlations for the GFS**
** ****with (Ops.+AIRS) and without (Ops.) AIRS Data**
**Southern hemisphere, January 2004**

## Forecast Improvement 24-hr. Fcst   925hPa RH

**Forecast Improvement 24-hr. Fcst   925hPa RH**

## AIRS Data Assimilation

**     ****1-31 January 2004**
**     ****Used operational GFS system as Control**
**     ****Used Enhanced Operational GFS system Plus AIRS**
**              ****as Experimental System**
**     ****First example of significant positive impact **
**              ****both N and S Hemispheres**

**AIRS Data Assimilation**

## Hyperspectral  Data Assimilation

-  JCSDA is currently undertaking studies to document
-  the effects of data spatial density and spectral coverage
-  on hyperspectral radiance assimilation

## Hyperspectral  Data

** ****Assimilation**
**JCSDA is well prepared for assimilating**
**CrIS hyperspectral radiance data.**

## Using AVHRR and MODIS data

**In Preparation for**
**               ****the VIIRS**

## NPOESS Instruments – VIIRS

**AMS 2006 - Future National Operational Environmental Satellites Symposium**

**Risk Reduction for NPOESS Using Heritage Sensors    ****      **

**The Visible/Infrared Imager/Radiometer Suite collects visible/infrared imagery and radiometric data.  Data types include atmospheric, clouds, earth radiation budget, clear-air land/water surfaces, sea surface temperature, ocean color, and low light visible imagery. Primary instrument for satisfying 27 environmental data records (EDRs).**

**NPOESS Instruments – VIIRS**
**Visible/Infrared Imager/Radiometer Suite**

**Heritage and Risk Reduction**

**POES**

**  ****- **

**Advanced Very High Resolution Radiometer**

**(AVHRR/3)**

**DMSP**

** ****-**

**Operational Linescan System (OLS)**

**EOS**

** ****-  **

**Moderate Resolution Imaging Spectroradiometer**

**(MODIS)**

**NPP**

** ****- **

**Early validation of operational instrument and**

**algorithms**

**VIIRS Sensor Bands**

**Imaging Optics: 18.4 cm Aperture, 114 cm Focal Length**

**Band**

**-**

**to**

**-**

**Band Registration (All Bands, Entire Scan)**

**> 80% per Axis**

**Orbital Average Power: 240 Watts**

**Mass: 275 Kg**

**VIIRS**
**Key Characteristics and Performance**

## Improved NCEP SST AnalysisXu Li, John DerberEMC/NCEP

**	****             ****Project Objectives****	****To Improve SST Analysis****	****Use satellite data more effectively****	****Resolve diurnal variation****	****Improve first guess**

##  JCSDA has developed an operational physical SST

-  **JCSDA has developed an operational physical SST**
** ****estimation algorithm suitable for use with VIIRS data**

## MODIS Wind Assimilation into the GMAO/NCEP Global Forecast System

**John Le Marshall (JCSDA)**
**James Jung (CIMSS)**
**Tom Zapotocny (CIMSS)**
**John Derber (NCEP)**
**Jaime Daniels (NESDIS)**

## AMV

**AMV**
**ESTIMATION**
**11****μ****m and 6.7 ****μ****m gradient features tracked**
**Tracers selected in middle image**
**Histogram, H****2****O intercept method, forecast model and auto editor used for height assignment**

## Water Vapor Winds

**Water Vapor Winds**

**05 March 2001: Daily composite of 6.7 micron MODIS data over half of the Arctic region. Winds were derived over a period of 12 hours. There are about 13,000 vectors in the image. Vector colors indicate pressure level - yellow: below 700 hPa, cyan: 400-700 hPa, purple: above 400 hPa.**

**Low  Level**
**Mid  Level**
**High Level**

## Global Forecast System Background

**Operational SSI (3DVAR) version used **
**Operational GFS T254L64 with reductions in resolution at 84 (T170L42) and 180 (T126L28) hours. 2.5hr cut off**
**Winds assimilated only in **_**second last analysis**_** (later “final” analysis) to simulate realistic data availability.**

## Satellite data used operationally within the 

**Satellite data used operationally within the **
**GMAO/NCEP   Global Forecast System**

| 
HIRS sounder radiances
AMSU-A sounder radiances
AMSU-B sounder radiances
GOES sounder radiances
GOES 9,10,12, Meteosat
atmospheric motion vectors
GOES precipitation rate
SSM/I ocean surface wind speeds
SSM/I precipitation rates | 
TRMM precipitation rates 
ERS-2 ocean surface wind vectors
Quikscat ocean surface wind vectors
AVHRR SST
AVHRR vegetation fraction
AVHRR surface type
Multi-satellite snow cover
Multi-satellite sea ice
SBUV/2 ozone profile and total ozone
 |
| --- | --- |

## Results

**Northern Hemisphere / The Arctic**

## Slide 59

## Southern Hemisphere / Antarctica

## Slide 61

## 2004 ATLANTIC BASIN

**AVERAGE HURRICANE TRACK ERRORS (NM) **

**2004 ATLANTIC BASIN**

## Discussion and Conclusions

**  ****Overall positive impact**
**  ****Post NESDIS QC used, particularly for gross errors cf.**
**	****  ****background and for winds above tropopause**
**  ****Implemented in  JCSDA Partner Organisations**
**          ****JCSDA is ready for VIIRS polar wind assimilation**

## MODIS WINDS v2. Improved winds/QC use of EE

**AMS 2006 - Future National Operational Environmental Satellites Symposium**

**Risk Reduction for NPOESS Using Heritage Sensors    ****  **

**MODIS WINDS v2. Improved winds/QC use of EE**

## Polarimetric Radiative Transfer 

**Using SSMI, SSMIS, WindSat AMSR(E) data in**
**Preparation for a**
**               ****Scanning Imager/Sounder  **

## SSMIS Radiance Assimilation

**NCEP Global Forecast System**
**10 August  -  10  September  2005 **
**NCEP  GFS  Valid  September 2006**

## SSM/IS Radiance Assimilation in GSI

- Period:00z 10 Aug.-00z 10Sep. 2006

- Assimilation System:
-  GSI 3D-Var
- Forecast model:
- NCEP Operational global model (Sep.2006)
- Resolution:
-  T382L64
- Data:
-   EXPC: Operational
-   EXPS: Operational + UKMO SSMIS data
-                           (removed flagged data)

**Preliminary Results**:
-  Improved A.C. 500 hPa height.
-  Requires further investigation of data quality

## NCEP AMSR-E Radiance Assimilation

- Period
-    4-week cycling (Aug. 12, 2005 - Sept. 11, 2005)
- System
-    Analysis: GSI ( May. 2006 release version)
-                 + New MW Ocean emissivity model
-    Forecast: Operational forecast model
-                    Resolution: T382L64
- Data set
-    Cntl: same as operational
-    Test: Cntl + AMSR-E radiance data

## AMSR-E Radiance Assimilation in GSI

- Control

- Test1

- Test2

- Period:00z 12 Aug.-00z 11 Sep. 2005

- Assimilation System:
-  GSI 3D-Var
- Forecast model:
- NCEP Operational global model (May.2006)
- Resolution:
-  T382L64
- Data:
-   Control: Operational
-   Test1: Operational + AMSR-E (FASTEM1)
-   Test2: Operational + AMSR-E (New EM)

**Preliminary Results:**
-  Improved A.C. 500 hPa height.
-  Decrease of RMS of surface wind speed analysis increment

## USE OF SURFACE WIND VECTORS AT THE JCSDA

- J.Le Marshall

## JCSDA WindSat Testing

- Coriolis/WindSat data is being used to assess the utility of passive polarimetric microwave radiometry in the production of sea surface winds for NWP
- Study accelerates NPOESS preparation and provides a chance to enhance the current global system
- Uses NCEP GDAS

## JCSDA WindSat Testing

- Experiments
**Control with no surface winds (Ops minus QuikSCAT)**
**Operational (QuikSCAT only)**
**WindSat only**
**QuikSCAT & WindSat winds**
- _Assessment underway_

## WindSat v Ops - QuikSCAT

**Notes:**
Ops(no AIRS, no aqua amsu, quikscat), Ops + windsat

## Windsat – Impact on operations

**Notes:**
Ops(no AIRS, no aqua amsu, quikscat), Ops + windsat

## Windsat – Impact on operations

**Notes:**
Ops(no AIRS, no aqua amsu, quikscat), Ops + windsat

## Assimilation of GPS RO observations at JCSDA

**Lidia Cucurull, John Derber, Russ Treadon, Jim Yoe...**
**	****	**

## Using 

**COSMIC GPS Data in**
**Preparation for the**
**               ****GPSOS***

## GPS RO / COSMIC 

- 24 transmitters

- 6 receivers

- 3000 occultations/day

## GPS RO /COSMIC :

**   ****The **_**CO**_**nstellation of **_**S**_**atellites for**
**      **_**M**_**eteorology, **_**I**_**onosphere, and **_**C**_**limate**
**   ****A Multinational Program**
**   ****Taiwan and the United States of America**
**   ****A Multi-agency Effort**
**   ****NSPO (Taiwan), NSF, UCAR,**
**	****	****NOAA, NASA, USAF**
**   ****Based on the GPS Radio Occultation Method**

## GPS RO / COSMIC :

**Goals are to provide:**
**Limb soundings with high vertical resolution**
**All-weather operating capability**
**Measurements of Doppler delay based on temperature and humidity variations, convertible to bending angle, refractivity, and higher order products (i.e., temperature/humidity)**
**Suitable for direct assimilation in NWP models**
**Self-calibrated soundings at low cost for climate benchmark**

## Information content from1D-Var studiesIASI (Infrared Atmospheric Sounding Interferometer)RO (Radio Occultation) - METOP

**(Collard & Healy, QJRMS,2003)**

**Notes:**
Normalized error is an indication of how the measurement has improved upon the forecast.  It is the measurement error divided by the forecast error.  So if the forecast error is 1K and the measurement error is 0.5K, the normalized error is 0.5.  The forecast error varies with altitude and latitude; it is ~1.2K in the extratropics and ~0.9K in the tropics.

A normalized error of less than 1.0 improves the forecast.

## GPS RO / COSMIC (cont’d):

**COSMIC launched April 2006**
**Lifetime 5 years**
**Operations funded through March 08**

**COSMIC data to be assimilated operationally after move to new GSI analysis**

## Early impact experiments (T382) with COSMIC

-   Anomaly correlation as a  function of forecast day for two different experiments:
- E (assimilation of operational obs),
- BND (E  + COSMIC bending angle).
-   Only COSMIC observations available in operations have been used in BND.
-  Only COSMIC observations < 30 km
-   These results might have been impacted by the development stage of the GSI system

## Fit to Radiosondes: November 2006

- Solid lines: CTL
- Dashed lines: CTL +   bending angle

## Summary

**NPOESS will provide higher spatial and spectral resolution data for environmental and climate applications**
**These data, used with modern data assimilation methods, will lead to significantly improved weather, ocean, climate and air quality forecasts.**
**Experience has shown for early data exploitation it is vital JCSDA is involved in CAL/VAL activity and early data evaluation.**
**Community RTM and emissivity model being expanded to include NPP then NPOESS instruments.**
**Risk Reduction/OSSE Studies have been undertaken in support of NPOESS**
**Work on AIRS, AMSU, AVHRR and MODIS assimilation as a prelude to using CrIS, ATMS and VIIRS on NPOESS is ongoing.**
**Assimilation of GPS (CHAMP/COSMIC/GPSOS/GRAS) well advanced and will improve upper troposphere reanalyses.**

## Summary

** ****Preparation for a polarimetric scanner/imager well underway using SSMI, SSMIS, AMSR(E) and WINDSAT observations. **
**OMPS will be added to O****3**** sensing suite**
**Modern data assimilation methods will aid in calibration/cross calibration, QC, climate analysis (use asynoptic obs),...... **
**Some important  environmental/climate observations still require attention**
- Aerosols
- Solar irradiance
- Sea level
- Earth radiation budget

## Closing Remarks

**The next decade of the meteorological (multipurpose) satellite program promises to be as exciting as the first, given the opportunities  provided by new observations, modern data assimilation techniques, improved environmental modeling capacity and burgeoning computer power.**
**NPOESS will provide essential observations for improved environmental (ocean, atmosphere, climate) modeling and for improved climate data sets**
**JCSDA will be well positioned to exploit the NPOESS component of the GEOSS in terms of:**
**Assimilation science**
**Modeling science.**
**Computing power**

## The business of looking down 

**The business of looking down **
**is looking up**

## Slide 89