# Presentation: 904352

## MOPITT Retrieval of Carbon Monoxide and MethaneJohn GilleNational Center for Atmospheric Research

## MOPITT Team

- James Drummond
- Gurpreet Mand
- Eamonn Mc Kernan
- Boyd Tolton
- Zhen Yu
- Jianshen Zhu

**NCAR**

**University of Toronto**

- John Gille
- David Edwards
- Jarmei Chen
- Merritt Deeter
- Louisa Emmons
- Gene Francis
- Eric Fry
- Shu-Peng Ho

- Boris Khattatov
- J.F. Lamarque
- Debbie Mao
- Jianguo Niu
- Juying Warner
- Valery Yudin
- Dan Ziskin

## 	- Level 1 data products
	         - Calibrated and geo-located radiances
	- Level 2 data products
	         - Tropospheric CO profiles with a 22 km horizontal resolution 
                 Mixing ratios at 4 km vertical resolution and 10% precision
               - CO total column with 10% precision
               - CH4 total column with 1% precision
	- Level 3 data products 
               - Gridded global CO distribution 
               - Gridded global CH4 distribution 

**	****         ****-**** ****Calibrated and geo-located radiances**
**	****- Level 2 data products**
**	****  ****       ****- Tropospheric CO profiles with a 22 km horizontal resolution **
**                 ****Mixing ratios at 4 km vertical resolution and 10% precision**
**               ****- CO total column with 10% precision**
**               ****- CH****4**** total column with 1% precision**
**	****- Level 3 data products**** **
**               ****- Gridded global CO distribution **
**               ****- Gridded global CH****4**** distribution **

**MOPITT Standard****Scientific Products**

##  Data Reduction Algorithm

**FORWARD MODEL:****  MOPITT signals are modeled using the MOPFAS operational fast forward model based on a regression technique and transmittances from the GENLN2 code**
**RETRIEVAL:**** MOPRET is a maximum likelihood algorithm which combines information from the instrument and prior measurements. These come from from in situ measurements and the output of the NCAR chemical-transport model MOZART **
**CLOUD DETECTION:**** MOPCLD uses radiance threshold methods with filtering algorithms based on  the N* technique for cloud clearing**

- Data processing takes place in two stages:
-  Level 0-1: Instrument Counts                  Calibrated Radiances
-  Level 1-2: Calibrated Radiances              Retrieved Geophysical Products

**Level 1-2 has three main elements:-               **

## Retrieval of Information from MOPITT Data

**CO is present in the atmosphere in one part in 10 million.  We are trying to achieve accuracies and resolve differences of the order of one part in 100 million.  **
**The measured radiances must agree with calculated values to much less than 1% to achieve these accuracies, especially in the lower troposphere.**
**A large fraction of the difficulties in producing validated data were due to discovering small effects in the instrument in orbit that necessitated changes to the L1 processor, changing the radiances input to the L2 retrieval processor**

**Retrieval of Information from MOPITT Data**

## Retrieval Sensitivity to Radiance Errors

## The MOPITT Level 0-1 Processor(Raw counts to Geolocated Radiances)

**L0 Data**

**Earth View**
** ****or Cal Event**

**MOPCH**

**Signal Calculation**

**Geolocation**

**Verification cf. Engineering data**

**Calibration**

**L1 Data**

**Reads MOPITT L0 count data**
**Separates space and black body calibration                         target signals from Earth view signals**
**Calculates correlation cell Average and Difference signals**
**Geo-locates signals using satellite ephemeris data **
**Verifies against engineering data**
**Calibrates the Earth view signals**

## A and D Response Functions

## MOPITT Level 1b DataCalibrated Radiance Product

**Geolocated radiances for each MOPITT channel average and Difference signal **
**Strong dependence on surface temperature which dominates the signal variation**
**Clouds appearing cold**
**Diurnally varying signal**
**Not particularly suited to research applications without further processing to Level 2**

**MOPITT Level 1b calibrated signal data shows:**

## Forward Model

**Used to calculate the upwelling radiance as a function of **
**	****frequency ******** and atmospheric state variables**
**	****	****T - temperature**
**	****	****m******** - mixing ratio of the i****th**** atmospheric trace gas**
**	****	****	****N****2****O****	****	****–  constant interfering species**
**	****	****	****H****2****O, O****3****	****	****–  variable interfering species**
**	****	****	****CO, CH****4****	****–  target gases**
**Required for retrieval, since we must modify the amount of target gas, subject to constraints, to make calculated radiance agree with measured value**
**	****	********  ****High degree of accuracy****	**

## The MOPITT Level 1-2 Processor

- L1

- NCEP

- Clim

-  Input

-  Cloud

-  Retrieval

- L2

**Met data & climatology are interpolated in time & space to match L1 observation**
**The L1 data and the ancillary data are passed to the cloud detection module**
**Only the clear pixels are passed to the retrieval module**
**The retrieval module calculates the abundance of carbon monoxide and methane**

**After the Level 0-1 Processor has converted instrument counts into calibrated channel radiances, the Level 1-2 Processor retrieves the final geophysical CO and CH4 data products**

- Forward Model

## MOPITT Cloud Algorithm

**Clouds are detected using threshold tests which check thermal and solar radiance differences between observations and model calculations **
**An equivalent cloud top pressure can be estimated using reflected solar radiation from the CH****4**** channels**
**N* methods will be used to clear non-uniform cloud in the FOVs**
**Overcast cloud situations will be characterized to allow CO profiles to be retrieved above cloud tops**

##        Clear scenes detected by MOPITT cloud algorithm overlaying a MODIS visible image. Colors shows the MOPITT thermal radiance

**MOPITT Cloud Validation**

-        **Clear scenes detected by MOPITT cloud algorithm overlaying a MODIS visible image. Colors shows the MOPITT thermal radiance**

**MOPITT and MODIS co-location.        The image is a MODIS cloud mask and the plot the MOPITT footprints. Color shows the MOPITT thermal radiance**

**MOPITT cloud detection algorithm agrees well with MODIS cloud mask**

## Radiative Transfer Equation

**At each frequency, the upwelling radiance at the instrument platform is****:**

**Where ****	****       transmittance to space as function of altitude**
**	****B = Planck function**
**	****Subscripts  denotes surface conditions**
**The surface term**

**Where        = surface emissivity**
**	****     ****= downwelling radiance at the surface**

## Radiative Transfer Equation

**The monochromatic transmittance to space is calculated from**

**Where:****	****	****= Zenith angle of the radiation**
**	****	****	****= Atmospheric density**
**	****	****	****= Absorption coefficient of i****th**** gas**
**The challenge in the IR comes from the rapid variation of k with ********i**** because ****	**** of the presence of spectral lines.**
**Many methods  used in past to deal with this, including band models.**
**Method of choice now is to do ****“****line by line****’’**** integration across the spectrum to calculate transmittance and upwelling signals.**

## Solar Signals

**For Solar Channels, 2nd term in equation for I****s**** dominates surface term in equation for I , which becomes for each frequency**** **

## Upwelling Radiance at the Top of the Atmosphere

## MOPITT Bands

## Correlation Radiometry:Average and Difference Signals 

**Resulting**
**Average**
**&**
**Difference**
**Responses**

**Two MOPITT signals are possible:                                                                 ****Average Signal:****         Mean of the low and high opacity signals                       ****Difference Signal:****      Difference of low and high opacity signals     **
**Average response is low at target gas line positions                                ****	****	****	****Information about background radiance and contaminating gas signal**
**Difference response is only significant at target gas line positions                         ****	****	****Effective high-resolution spectral filter to information about target gas                                                                                      **

## Correlation Radiometry:CO Profile Information

**Absorption of upwelling radition in the line wing takes place at low altitudes where pressure broadening is greater**
**Radiative processes that define the line center take place higher up in the atmosphere**
**The correlation cell pressure determines the part of the line wing that is sampled **
**This allows information about the vertical profile to be obtained by using several PMR and LMR cells of difference pressures**

## Radiative Transfer

**Frequency averaged radiance at the top of the Atmosphere given by**
**If            is the instrument response for Average and Difference Signals.**
**Average Signal**
**     **
**    ****>**
**Difference Signal**
-                 **<**

## Radiative transfer equations for MOPITT thermal channels (4.7 μm)

Radiative transfer equations for MOPITT solar channels (2.3 μm)

- Radiative transfer equations for MOPITT thermal channels (4.7 μm)
- Radiative transfer equations for MOPITT solar channels (2.3 μm)

## Retrieval Requirements

**Requires:**
**Accurate and Fast Radiance Calculation Model**
**     ****(Forward Model)**
**Input Data:**
**     ****Vertical Temperature Distribution - (NCEP)**
**     ****Vertical Water Vapor Distribution - (NCEP)**
**     ****Climatological Distribution of Fixed Gases (e.g.          )**
**     ****Surface Reflectivity/Emissivity**
**Since    **

## Weighting Functions 

**  ****MOPITT CO channel weighting functions**
**   **
**   ****(a)  Thermal channel average signals**
**   ****(b)  Thermal channel average signals and**
**          ****solar channel Avg./Diff. signal**

**  ****Characteristics**
**    **
**   ****(1)   Low pressure channels peak higher**
**   ****(2)   High pressure channels peak lower**
**   ****(3)   Solar channels **_**peak at the surface**_

## Sensitivity to Surface T & 

## MOPITT L1 Validation Biased Radiances: Solution

**Bias statistics were used to determine the filter shift values**

**L1 & L2 processors were revised to incorporate filter shifts, and new radiances were rechecked against forward model**

**A set of MOPITT nighttime radiances was formed for scenes over the Indian Ocean where biases relative to modeled radiances were observed to be most consistent**

## Retrieval Overview

**Retrieval uses Maximum Likelihood Method of Clive Rodgers (Rodgers, 2000)**
**Solution is weighted mean of retrieval solution and *****a priori***** information**
**Weighting is by inverse of the variance-the less certain the number, the less it contributes**
**Fundamental quantity is ratio of instrument noise level to variance in *****a priori***** information. **
**Manipulation leads to concept of Averaging Kernel Matrix A**

## Maximum Likelihood Approach

## Formulation of the Problem

**Carbon Monoxide Forward Equation**

**Measurement vector**

**State Vector**

## MOZART2 global profile and MOPITT a priori. CO profiles are shown by dashed lines. Blue lines show variance in % 

## Optimal Estimation Retrieval

**Where   **
**Combining with a prior information as virtual measurement, **
**     ****(Rodgers, 1976) leads to**
**With covariance**

##  Retrieval Variances and Uncertainties

## Role of the 

**Role of the **
**Averaging Kernel**

**Where**
** ****	****= retrieval solution**
**A****	****   averaging kernel**
**X****0****	****   a priori distribution**

## Averaging Kernels

**The first seven averaging kernels for the CO profile retrieval using a two kilometer atmospheric layering for 2 noise values.  The aircraft ensemble statistics were used in the inversion.  **

## MOPITT Averaging Kernels

**MOPITT Averaging Kernels**

## MOPITT Averaging Kernels

## Retrieval Algorithm

**      ****Description**

**X****n****=  estimated atmospheric CO profile at iteration n       X****o****=  background (or a priori) CO profile**
**Y =  calculated radiance from forward model                   K****n**** = Weighting function at iteration n**
**Y****m****= measured radiances by instrument                            S****x****=   background profile covariance matrix**
**S****   ****=  instrument noise covariance matrix**

**X****o****,S****x**

**Y**

- (

- )

**-**

**-**

**+**

**-**

**+**

**X****n**

**=**

## Description of V2 Pre-Anomaly Data

**Factors Affecting Data Quality**
**4.6 ********m filter positions adjusted for - SST agreement**
**                                                            ****- inner/outer pixel positions**
**Closest previous calibration noise values used***
**Average and difference signals calibrated***
**Only 3 thermal channels (1,3,7) used**
**No data from solar channels used - No CH****4**
**                                                            ****Possible effects on near surface CO**
**Factors Affecting Data Coverage**
**Clouds determined from MOPITT channel 1 radiances***
**   ****Limited to  ********  65****0****  ****latitude**
**2 stares/5 in packet not used because systematically biased ****	****	**
**relative to other stares***

## MOPITT Level 2 DataCO Total Column Product

**MOPITT Level 2 CO Column data**

**CO Total Column x10****16**** (mol/cm****2****)**
**                 ****3/16-18/2000**

## Slide 40

## Slide 41

## Slide 42

## Terra Detection of Montana Fires

**Terra Detection of ****Montana Fires**

**MODIS Aug. 23 2000**
- True-color image of smoke & burn scars over Montana. Fire pixels from 3.75 μm channel (*Rong Rong Li)*

**MOPITT Aug. 22-27 2000**
- Continental total CO column.
- Elevated levels over Montana

## Comparison of V2 MOPITT& TRACE-P Profiles

**Comparison of V2 MOPITT& TRACE-P Profiles**

## Goals for V3

**Improve CO Accuracy**
**More detailed calibration**
**Increase Coverage**
**Extend to 80****0**** Latitude**
**Use MODIS Cloud Mask**
**Recover all 5 stares in a packet**
**Stare-by-stare calibration**

## Coverage ComparisonV2 (L) and V3 

## Comparison of V3 MOPITT& TRACE-P Profiles

## V3 Validation Comparison

**V3 Validation Comparison**
**With Aircraft Data**

## CO Column 1-3 June 2002

**CO Column 1-3 June 2002**
**(Phase 2 Data)**

## Add Methane Data

Improve CO Data
Accuracy
More detailed calibration of PMC’s
Incorporate more channels

**Improve CO Data**
**Accuracy**
**More detailed calibration of PMC’s**
**Incorporate more channels**

**Future Directions- ****Data Development**

## Science Applications
Mean distributions and interannual variations
Source locations and strengths
Urban sources
CO as a dynamical tracer
Data assimilation -
Predict unmeasured quantities
Assimilate data from other sources

**Mean distributions and interannual variations**
**Source locations and strengths**
**Urban sources**
**CO as a dynamical tracer**
**Data assimilation -**
**Predict unmeasured quantities**
**Assimilate data from other sources**

**Future Directions (2)**

## SUMMARY

**MOPITT Data are providing an unprecedented **
**	****view of CO in the Troposphere**
**Showing spatial and temporal variations on global basis.**
**V3 Data show**
**Much improved accuracy, esp. at low levels **
**Doubled coverage (higher latitudes, more pixels)**
**V3 Data now being publicly released**
**More information at **
**	****http://eos.acd.ucar.edu/mopitt/home.html**
**	****Updates to appear ****	**