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By Murty Divakarla, Eric Maddy, Mike Wilson, Xiaozhen Xiong, Changyi Tan,

Dusty, Dust-Free Retrievals From CrIMSS and AIRS Retrieval Algorithms. By Murty Divakarla, Eric Maddy, Mike Wilson, Xiaozhen Xiong, Changyi Tan, Antonia Gambacorta , and Nick Nalli IM Systems Group, Inc., at NOAA/STAR Chris Barnet NOAA/STAR Degui Gu, Xia L Ma NGAS

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By Murty Divakarla, Eric Maddy, Mike Wilson, Xiaozhen Xiong, Changyi Tan,

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  1. Dusty, Dust-Free Retrievals From CrIMSS and AIRS Retrieval Algorithms • ByMurty Divakarla, Eric Maddy, Mike Wilson, Xiaozhen Xiong, Changyi Tan, • Antonia Gambacorta, and Nick Nalli • IM Systems Group, Inc., at NOAA/STAR • Chris Barnet • NOAA/STAR • Degui Gu, Xia L Ma • NGAS • Xu Liu and Susan Kizer • LaRC

  2. Proxy Data Sets vs. Real Observations Proxy Data Set Generation • Utilize Sensor Data Records (SDRs) from an existing satellite instrument suite (IASI/AMSU-A/MHS -> to CrIS/ATMS) to generate proxy SDRs for future satellite instruments. • Pre-Launch use • Develop and test algorithms to produce retrieval products • Select channels for the physical retrieval algorithms • Define and test channel sub-sets • Post-Launch phase • Improve procedures developed with pre-launch proxy SDRs to optimize Post-Launch EDR Algorithm (research to operations). • Thus, evaluating results obtained from the proxy data sets with results from real satellite observations helps to understand the limitations of the proxy data algorithms and to suggest improvements for future utility. • The attempt here is to present one such example - effect of dust on two different retrieval algorithms – How it was perceived with proxy data, and how it is seen with real observations.

  3. Retrieval Algorithms and Data Setsfor Dust Effects/Evaluation(Earlier Attempts with CrIS/ATMS Proxy Data) Using Proxy CrIS/ATMS SDRs and IASI/AMSU-A/MHS SDR • Retrieval Algorithms • CrIMSS (CrIS/ATMS) official retrieval algorithm • NOAA has Off-line Version to generate CrIMSS products - T(p), q(p), O3(p)/ Also ADL • AIRS Heritage Algorithm • Implemented as a part of the NOAA-IASI (IASI/AMSU-A/MHS) retrievals • Data Sets used to Evaluate Dust Effects • IASI-AMSU-A/MHS and corresponding CrIS/ATMS proxy data matches with ECMWF • CrIMSS official CrIS/ATMS RET Products (with proxy CrIS/ATMS SDRs) • NOAA-IASI RET Products (Based on AIRS Heritage Algorithm) • Discussion of Results presented at AIRS Science Team Meeting – November 2011 (Murty’s talk)

  4. Retrieval Algorithms and Data Setsfor Dust Effects/Evaluation(Current Research with Real CrIS/ATMS SDRs) Using Matched CrIS/ATMS SDRs and AIRS/AMSU-A SDRs • Retrieval Algorithms • CrIMSS (CrIS/ATMS) official retrieval algorithm • NOAA has Off-line Version, IDPS-EDRs, ADL • CrIMSS EDR products - T(p), q(p), O3(p) • AIRS Heritage Algorithm • Implemented as a part of the NOAA-NUCAPS (CrIS/ATMS) retrievals • Data Sets used to Evaluate Dust Effects • Matched CrIS/AIRS SDRs and corresponding retrievals for he focus day (05/15/2012) • CrIMSS official CrIS/ATMS RET Products • AIRS RET Products • V5.9, V6 (with pgood QC, and pbest QC) • Discussion Follows on Similarities observed with CrIS/ATMS proxy retrievals and the EDRs generated with real CrIS/ATMS observations.

  5. CrIMSS EDRs and Aqua-AIRS Retrieval Matches

  6. Aqua-AIRS and NPP-CrIMSS Matches • Match CrIMSS SDRs/EDRs with Aqua-AIRS SDRs/EDRs • About 177,000 Matches of AIRS/CrIS FORs for 05/15/2012 • Utilize Common Set of Truth Data Sets • ECMWF/NCEP-GFS • Dedicated RAOBs (05/15, analysis/discussion next time) • Global Operational RAOBs (presentation planned in future) • Verify CrIMSS EDR Statistics and Aqua-EDR statistics on the Matched EDRs using ECMWF as the reference • Globally about 4000 clear cases can be obtained from the AIRS Retrievals, and corresponding matches of Aqua-AIRS/AMSU and CrIS/ATMS SDRs at 3 x3 FOVs, and matched retrievals at FOR resolution can be extracted. • These clear cases are most-useful in evaluating the CrIMSS EDRs with ECMWF, and enhancing the bias-tuning efforts.

  7. Aqua-AIRS and NPP-CrIMSS Versions • Aqua-AIRS Retrievals • Version 5.9 uses regression based on PCs trained with ECMWF as the first guess for the final physical retrieval. • Version 6 uses Neural Network (NN) regression trained with ECMWF as the first guess for the final physical retrieval. • Uses pressure dependent QC for each profile, (1) high thresholds for data assimilation applications (pbest) , (2) loose thresholds for climate applications (pgood). The yield is higher with pgood, and yield is lower for pbest. • We don’t have exact QC, but we did somewhat similar • QC = 0 or 1 up to 750 hPa (higher yield, pgood must be at least 750hPa) • QC = 0 up to 750 hPa (lower yield , pbest must be at least 750 hPa) • Used T(p) QC control for both T(p) and q(p) • CrIMSS Retrievals. • Past (MX5.3), Present (MX6.3), Upcoming (MX7) • Retrievals used here are some what MX7 equivalent.

  8. ‘Dust-Free ’ vs. ‘Dusty’ Granule Proxy Retrievals07/28/2011, 08/01/2011 IASI and CrIMSS(Murty, NASA Sounder Sci. Meeting , Nov. 2011) CrIMSS T(p) Retrievals are Resilient to Dust?

  9. Dusty Samples from Focus Day 05/15/2012

  10. Matched AIRS/CrIMSS Retrievals±60 LAT, GLOBAL (L+S+C; D +N) Dust-Free Cases (remove samples with DS > 380) Solid Lines CrIMSS IR+MW Dashed Lines AIRS V5.9 RET AIRS V6 Climate AIRS V6 Assimilation N= 140171 Dust Free Matches AIRS:73% dashed AIRS: 92% dashed AIRS: 46% dashed CrIMSS:48% solid T(p) RMS (K) Q(p) RMS (%)

  11. Matched AIRS/CrIMSS Retrievals±60 LAT, GLOBAL (L+S+C; D +N) Dusty Cases (DS > 380) Solid Lines CrIMSS IR+MW Dashed Lines AIRS V5.9 RET AIRS V6 Climate AIRS V6 Assimilation N= 1400 Dusty Matches AIRS:54% dashed AIRS: 79% dashed AIRS: 22% dashed CrIMSS:24% solid T(p) RMS (K) Q(p) RMS (%)

  12. Matched AIRS/CrIMSS Retrievals±60 LAT, GLOBAL (L+S+C; D +N) (includes all Matches) Solid Lines CrIMSS IR+MW Dashed Lines AIRS V5.9 RET AIRS V6 Climate AIRS V6 Assimilation N= 141,000 Matches AIRS:73% dashed AIRS: 92% dashed AIRS: 45% dashed CrIMSS:57% solid T(p) RMS (K) Q(p) RMS (%)

  13. Results • Evaluation of AIRS and CrIMSS Retrievals with ECMWF • Plots shown are for RMS differences. Bias plots are available if any one wishes to see. • Dusty Cases • AIRS 5.9 gets severely affected • AIRS V6 with pgood (0 or 1 up to 750hPa) moderately affected • AIRS V6 with pbest (0 up to 750hPa ) marginally affected • Although affect on T(p) is moderate to marginal in V6, Despite NN FG, the V6 water vapor retrievals get affected for dusty regions. • CrIMSS Retrievals show degradation with dust, but the effect is not as severe as the AIRS V5.9 retrievals. • Currently Analyzing GOCART model dust predictions (Collaboration with Sarah Liu)

  14. Results • Evaluation of AIRS and CrIMSS Retrievals with ECMWF • Dust-Free Cases • When Dusty cases (Dust Score > 380) are removed from the ensemble, the AIRS retrievals show marginal improvement, while CrIMSS retrievals show about two tenths of a degree improvement in RMS Difference with ECMWF. ‘Useful to have a dust flag in the CrIMSS SDRs/EDRs’ • AIRS V6 (with QC = 0 up to 750 hPa ) meets 1K/1Km even for cloud-cleared cases. AIRS defined ‘clear ‘cases (QC = 0 up to 750 hPa) are pretty good with an RMS of 0.8K or less (figures not shown) • This Matched AIRS-CrIS data set is very useful and can lead us to provisional maturity very easily, and can be considered as ‘Dedicated Matches’ synonymous to Dedicated RAOBs that are very sparse. • Evaluation of AIRS/CrIMSS/ECMWF EDR matches with Dedicated RAOB Ascent (Kauai, Hawaii) for 05/15 will be presented in the next Telecon.

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