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Microwave Sound Data Assimilation in NWP and Climate Research

Explore the impact of microwave sounding data from operational satellites on climate research and NWP systems. Learn about biases, improved assimilation, and assessment findings.

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Microwave Sound Data Assimilation in NWP and Climate Research

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  1. FY-3 satellite data tuning and assimilation Qifeng Lu National Satellite Meteorological Center, CMA, Beijing Email: luqf@cma.gov.cn ECMWF Thanks to all who contributed to this work

  2. Outline • Microwave sounding data in NWP and climate research: operational satellites from the US, Europe & China : 1978-2020 • China’s FY-3A satellite: The Microwave Temperature Sounder (MWTS) • Identifying and characterising MWTS biases using NWP fields: • Passband shifts • Radiometer non-linearities • Improved Assimilation of MWTS • Initial Assessment of FY-3B • Summary and conclusion • Next Steps: Early results from the evaluation of MSU and AMSUA from 1978-2011

  3. Operational Sounding Satellites US Europe China • Microwave sounding data provides information on temperature and humidity which has been widely used in : • Operational NWP data assimilation systems and; • Climate research – to determine long term trends in atmospheric state • The US has launched a series of polar satellites, dating back to 1978; • Europe began to contribute in 2006 (MetOp-A) • China began to contribute in 2008 (FY-3A)

  4. The importance of MW sounding data in NWP Largest positive impact (per system) is obtained from microwave temperature sounding data Forecast sensitivity to observations (FSO) Is an adjoint based technique for assessing the influence of observing systems on forecast accuracy (from C. Cardinali, ECMWF)

  5. The FY-3A/B Instrument Suite ok Microwave Radiation Imager 10 channels (~AMSR-E) Infrared Atmospheric Sounder (IRAS) 20 channels (~HIRS/3) Microwave Temperature Sounder (MWTS) 4 channel (~MSU) Microwave Humidity Sounder (MWHS) 5 channel (~MHS) 5

  6. Initial Data Quality Assessment at ECMWF: General Approach • Approach involves a comparison of observations (OBS) with simulated observations based on short range (up to T+9 hour) forecast fields (‘First Guess’, FG) and radiative transfer modelling → ‘FG departures’ • FG is ‘proxy’ for truth → ‘FG departures’ (OBS – FG) indicate error in the measurements or RT modelling • High accuracy of the NWP fields results from the large & diverse range of observations assimilated (MW sounders, Advanced IR sounders, GPSRO, radiosondes … etc) • Able to detect biases at ~0.1K level for temperature sounders (MWTS and IRAS), sensitivity slightly lower for MW humidity sounders & imagers (~0.5K) • Similar work ongoing at NOAA/NCEP, UK Met Office and DWD

  7. Initial FY-3A Evaluation

  8. Initial Data Quality Assessment at ECMWF: Comparison of FY-3A with MetOp & Aqua STDEV (first guess departures): measures the misfit between model & measurement (in TB space) Microwave Humidity Sounder Microwave Temperature Sounder Microwave Imager Infrared Sounder FY-3A data quality is comparable with MetOp/Aqua equivalents

  9. Initial Data Quality Assessment at ECMWF: Assimilation Experiments positive impact from FY-3A The impact of the FY-3A data is : • neutral in SH • slightly positive in the NH Full System OSEs: Full System + FY-3A VASS suite (MWTS, MWHS, IRAS) T511, 3 month experiment

  10. Characterising MWTS biases using NWP fields

  11. Characterize the MWTS Comparison of MWTS and AMSU-A Brightness Temperatures 2 1 State dependent inter-satellite (MWTS vs AMSU-A) biases:Region 1 (tropics): MWTS TBs warm relative to AMSU-ARegion 2 (high northern latitudes) : No significant biasBrightness temperature map from cycle 2008091700

  12. Schematic of error terms

  13. Characterising the FY-3A MWTS: Detecting and Correcting passband errors and Non-linearities First Guess Departures (K) ,i.e., Observation minus Simulation MWTS-3 MWTS-4 MWTS-2 Pre-launch measured passband Optimised passband • from line-by-line modelling • uncertainties of ~32-55 MHz • detected and corrected Non-linearity corrected AMSU-A equivalent

  14. Characterising the FY-3A MWTS: Assessing corrections in the CMA-GRAPES model Pre-launch measured passband Optimised passband Optimised + non-linearity corrected → the corrections, developed at ECMWF, improve the (OBS-GRAPES_MODEL) fits.

  15. Improvement due to MWTS data MWTS OSEs Forecast Verification: Z at 200, 500 and 700 hPa Normalised differences in RMS Errors in Z, verified against own analysis 90% confidence intervals shown Small improvements in SH in going from: original data → recalibrated (low weight) → recalibrated (high weight) NH close to neutral with some benefit in recalibrated data PRELAUNCH_MWTS (full system + original MWTS data) HIOBSERR_MWTS (Full system + optimised MWTS with low weight) LOWOBSERR_MWTS (Full system + optimised MWTS with high weight)

  16. MWTS: current status Ground system changes implemented at CMA, March 2011 FY-3A MWTS-4 MetOp AMSU-A Ch. 9 STD of 0.2 K is normally monitored

  17. Initial FY-3B Evaluation

  18. FY-3B MWTS FG Departure Channel 3: 54.072 GHz • Correction of passband measurement bias and radiometer non-linearity has been implemented in pre-processing at NSMC/CMA • No significant problems with the MWTS-2 and -3 observations • Cross scan bias is dominant & accounts for non-gaussian FG departures (corrected by variational bias correction scheme): After VarBC STDEV(O-FG) = 0.17 K

  19. FY-3B MWTS FG Departure Channel 4: 57.3353 GHz • Strong cross scan biases & regional biases detected • Variational bias correction unable to significantly reduce these biases • Work is ongoing to improve the correction of these anomalies

  20. MWTS FG Departure

  21. Summary • FY-3A data has been evaluated at ECMWF through a comparison with simulated radiances & full assimilation experiments in which FY-3A data is introduced in the ECMWF system. • The study revealed, and corrected, biases in FY-3A MWTS related to : • Uncertainties in the passband centre frequencies • Radiometer non-linearities • These corrections bring the MWTS data close to the quality of equivalent AMSU-A data & in assimilation experiments this MWTS data delivers improvements in forecast accuracy. • The high value of NWP in Cal/Val of new satellite sensors has been clearly demonstrated – further improvements in FY-3A and FY-3B data are expected, and it is hoped NWP will again play a crucial role for FY-3C, …. FY-3G ! • A powerful new general approach to diagnosing an important group of biases affecting microwave sounders has been developed. • This work demonstrates the significant benefits of close collaboration between satellite agencies and NWP centres.

  22. Next Steps: Initial results from an evaluation of MSU and AMSUA from 1978

  23. MSU CH3 (54.96 GHz): NOAA-6 to NOAA-14 Frequency Shift / MHz • Blue dots represent FG_DEP after shift correction • Colour dots before STD(FG_DEP) Improvement in STD(FG_DEP) • LbL modelling based on ERA-Interim fields • 1 cycle per month: 1979 -2011

  24. Finally … Thanks ! • We would also like to acknowledge the contribution of colleagues from across the world (from CMA, ECMWF, UK Met Office and others besides) who made this work possible • And finally - thanks to the local organizing committee for their hospitality here in the wonderful city of Kunming !

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