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RADIOSONDE DROPSONDE NWP

Observed UTLS Moisture Bias 1) Radiosonde 2) NWP Latest Suomi -NPP/NPROVS 1) Results 2) Moisture weighting (preliminary). MetOp -B ATOVS, MIRS, IASI, IASI (EU) GRAS. JPSS-NPP CrIMSS (2) MIRS . NASA-EOS-Aqua AIRS. NOAA-18 ATOVS, MIRS. RADIOSONDE DROPSONDE NWP. FORMOSAT-3, 4

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RADIOSONDE DROPSONDE NWP

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  1. Observed UTLS Moisture Bias 1) Radiosonde2) NWPLatest Suomi-NPP/NPROVS1) Results2) Moisture weighting(preliminary)

  2. MetOp-B ATOVS, MIRS, IASI, IASI (EU) GRAS JPSS-NPP CrIMSS (2) MIRS NASA-EOS-Aqua AIRS NOAA-18 ATOVS, MIRS RADIOSONDE DROPSONDE NWP FORMOSAT-3,4 COSMIC (UCAR) NOAA-19 ATOVS, MIRS DMSP F-16,17,18 MIRS GOES-11,13,12 IR Soundings MetOp-A ATOVS, MIRS, IASI, IASI (EU) GRAS Yes Plan No NOAA Products Validation System (NPROVS) Schematic

  3. NOAA Products Validation System (NPROVS) Centralized Radiosonde and Collocation Processing NPP Metop-B (dropsonde) 6-hour 250km (UCAR) (11,13 …R) Collocated radiosonde and multiple satellite products dataset NWP: GFS 6-hr CFSR CFSR (Back) Other April 2008 … S i n g l e C l o s e s t

  4. Following slides demonstrate typical differences in UTLS moisture among collocated radiosonde, nwp and satellite data Sources of data are: GRUAN (RS92 vs GFS vs Satellite) … 2011 WMO/CIMO Raob Comparison) (RS92 vs CFH) …. July 2010, S. China NPROVS ( Sondevs GFS vs NPP) … Dec, 2012

  5. … following slides show mean (no weighting) differences between identical RS92 conventional GTS processed sondes(red) vs RS92 GRUAN processed sondes( blue) GRUAN processing include: RH corrections Improved radiation correction Uncertainty etc

  6. ARM • ARM • ARM (1844) Sondes Collocations of GRUAN Sonde and GTS Sonde; 2011 … 70% within circle (50% LIN)

  7. All day Dusk Nite H20 Mixing Ratio (g/kg) % Difference

  8. UTLS Dry bias estimate up to 20% water vapor fraction for conventional vs GRUAN processed sonde during day

  9. Next slide shows H20 vapor fraction differences (no weighting) between concurrent RS92 and CFH and collocated GFS from CIMO S. China radiosondeIntercomparisons, Summer, 2010 RS92 differences from CFH can be upwards 30% too dry in vicinity UTLS and differences from GFS upwards of 50% … tendency for GFS too wet

  10. RS92 COSMIC vs GFS @101 levels RS92 vsCIMO Flight Mean @”101” levels +/- 3 hr • RS92-minus-CFH • @ 101 Lev RS92-minus-CFH @ 42 TOVS Lev brief results from CIMO

  11. … further comparison to NOAA GFS (nwp) show mean “local” differences between sonde and NWP (red) can be 100’s of percent @ UTLS, for example, in vicinity of GRUAN site at Lindenberg (where GRUAN RH uncertainties typically order of a few %) Also shown are NOAA IASI retrievals which show tendency to track the NWP

  12. Typical NPROVS distribution

  13. Lin 500km of Lin Eurasia June 2-9 2012, All conventional sondes, NPROVS

  14. ARM • ARM • ARM (1844) Sondes Collocations of GRUAN Sonde and GTS Sonde; 2011 … 70% within circle (50% LIN)

  15. vs Conv RS92 Mixing Ratio (g/kg) % Difference Mixing Ratio (g/kg) % Difference

  16. Vs Conv RS92 Mixing Ratio (g/kg) % Difference Differences vs GTS processed Raob

  17. TWP Lin

  18. Example illustrating UTLS bias @ Lin

  19. UTLS Zoom of previous slide

  20. Moving on to NPP / NPROVS

  21. … first NPROVS with complete IDPS, NUCAPS, MiRS

  22. Following 4 slides show: Moisture fraction bias (weighted, Raob) Satellite-minus-Radiosonde: -CrIMSS IDPS -CrIMSS NUCAPS -MiRS Stats shown for common samples with following constraints 1) No Sat QC 2) NUCAPS Passed

  23. No Sat QC vs GFS

  24. NO Sat QC vsRaob

  25. NUCAPS QC vsRaob

  26. NUCAPS QC vs GFS

  27. Perceived characteristic moisture performance varies using NWP vsraob as baseline for comparison, particularly in vicinity of UTLS Moisture from RaobvsNWP diverges above 300 (200)hPa Perceived satellite moisture bias not so dependent on satellite QC CrIMSS“tend” to track NWP moisture more than MiRSin UTLS …

  28. Lindenberg Moisture anomaly persists in latest NPP NPROVS sample ?

  29. Moist NWP signature in vicinity of UTLS, Lindenberg

  30. Some Results for: H20 vapor weighting Perceived H20 performance against RaobvsGFS Impact of Satellite QC

  31. No QC Nucaps QC

  32. Current w: Raob2 …RMS Raob … Mean NUCAPS QC No QC Pending w: Raob … RMS Raob … Mean Standard Deviation vsRaob

  33. w: Raob2 …RMS Raob … Mean NUCAPS QC No QC w: Raob …RMS Raob … Mean SD vs GFS

  34. Examples of IASI, AIRS and GFS “un-weighted” H20 vapor fraction % diff vsraobs April 08 to present … NPROVS/NARCS

  35. Results from “w: raob” approach are pretty close to Results from “un-weighted” approach (NARCS) Results versus NWP favorable for sat validation particularly aloft (UTLS) Redo using new Chris) layers

  36. Next slides quantify differences among: Current ( AIRS, IASI) … w: Raob2(H20) for RMS and Raob(H20) for mean (Mixed) Propose 1 ... w: Raob2(H20) for both RMS and Mean; SD= (RMS2 - Mean2)**.5 Propose 2 … w : Raob(H20) for RMS and Mean; SD as above Using recent NPROVS NPP collocations (December 2012) No SAT QC

  37. Mean Bias w: Raob2 Current w: Raob

  38. RMS Current (w: Raob2) w: Raob

  39. RMS w/QC w/QC

  40. Standard dev Current (mixed) w: Raob2 w: Raob

  41. RH vs % H20 Fraction courtesy Bomin Sun

  42. Temperature ready to go …..

  43. Monitoring Operational Products from Suomi-NPP usingNOAA Products Validation System (NPROVS) H • NPROVS provides routine inter-comparison of NOAA operational atmospheric sounding products against collocated global radiosondes • Plots show preliminary vertical statistics of satellite-minus-sonde RMS differences for “Test” temperate sounding products from Suomi-NPP which passed internal QC • Left plots show differences for the two suites of “infrared + microwave” based soundings and the right plots the three suites of “microwave only” based sounding currently under development • Although preliminary, results show overall good agreement among the respective products satellites • Results also mark the successful transition of NPROVS from NOAA Satellite Operational Facility (NSOF) to STAR (College Park) computers Significance: Contiguous satellite product validation promotes timely operational deployment of high quality CrIS and ATMS data for weather forecasting and climate

  44. SUMMARY Dry Bias (UTLS) ranges 10 to 30 % in conventional RS92 sondevs corrected GRUAN RS92 Sonde, however differences between corrected GRUAN RS92 sonde and CFH can range 30 to 50% (UTLS). Conversely, moist bias in NWP (GFS) in vicinity of UTLS can be 100’s of % locally made larger by using relatively dry sonde as truth; conversely using nwp as truth decreases perceived magnitude of moist bias Satellite products such as NPP CrIMSS and NUCAPS track nwp moisture whereas MiRS does not … Revised Moisture weighting will “double” the perceived error … NPROVS approaching stable environment for Suomi-NPP Temperature validation

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