Analysis of an Observing System Experiment for the Joint Polar Satellite System
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1 Analysis of an Observing System Experiment for the Joint Polar Satellite System S. Lord, George Gayno 1 and Fanglin Yang 1 24 November I. M. Systems Group at NCEP/EMC 1
2 Outline The Joint Polar Satellite System (JPSS) Observing System Experiments (OSEs) Evaluating Forecasts (examples) Evaluating JPSS OSE Classical Value added Summary 2
3 Current Satellite System Other systems are Geostationary (white), GNSSRO (radio occultation, not shown) 3
4 Future Joint Polar Satellite System (JPSS) Future polarorbiting system (red) will be 2 Orbit- 4 Sounder (2O4S) Other systems are Geostationary (white), GNSSRO (radio occultation, not shown) 4
5 Outline The Joint Polar Satellite System (JPSS) Observing System Experiments (OSEs) Evaluating Forecasts (examples) Evaluating JPSS OSE Classical Value added Summary 5
6 Observing System Experiments (OSEs) Purpose: to measure impact of designated observing systems within a Numerical Forecast System (NFS) Why: Adds accountability and can be used to guide strategic decisions for spending on (expensive) observations How: Execute NFS with full complement of observing systems (control) and without one or more systems (expts) E. g. radiosondes, polar-satellites, GNSSRO 6
7 Observing System Experiments (OSEs) (cont) What: Numerical Forecast System (NFS) contains Observing Systems (OS) Data assimilation (DA) system Forecast model (Global Forecast System model, GFS) Verification (standard scores) Remarks: Results depend on both observations and NFS model and DA Statistical significance can be hard to achieve GFS is very accurate so small changes to OS may not produce large impacts Results can be noisy, and seasonally, hemispherically and case dependent OSEs are expensive due to DA and periodic forecasts and require expensive High Performance Computer resources Other techniques are potentially useful (e.g., Forecast Sensitivity to Observations, FSO) 7
8 Operational GFS (2013) JPSS OSE T574/L64 (27 km) model 3-D hybrid DA Daily assimilation cycle (6 hourly) Assimilation began 15 July Feb 2013 (7 months) Model runs 00, 06, 12, 18 UTC until 3 November 2012, 00 UTC only thereafter Evaluated 00 and 12 UTC runs beginning on 1 August 2012 Covers 3 seasons 293 cases (199 at 00 UTC, 94 at 12 UTC) Experiments Control (all JPSS-relevant observations) NOPM (remove NOAA polar-orbiting data in PM orbit) Operations is bonus extra 8
9 Observations JPSS OSE (Cont) Conventional (radiosondes, surface, aircraft, marine) Geostationary (GOES, Meteosat) GNSSRO (COSMIC, etc) OSE Polar Observing System Orbit Control (CNTL) No PM Orbit (NOPM) AMVs (Aqua) PM Yes No AMVs (Terra) AM No No MetOp-A ASCAT Mid-AM Yes Yes MetOp-A IASI Mid-AM Yes Yes MetOp-A AMSU-A Mid-AM Yes Yes MetOp-A MHS Mid-AM Yes Yes Aqua AIRS PM Yes No NOAA-19 AMSU-A PM Yes No NOAA-19 MHS PM Yes No OPS additions Polar Observing System Orbit Aqua (AMSU-A) PM NOAA-18 (AMSU-A, MHS) PM NOAA-15 (AMSU-A) PM AM NOAA-17 (HIRS) PM NOAA-19 (HIRS) PM Other related work McNally (2012) Cucurull and Anthes (2015) Boukabara et al (2015) 9
10 Outline The Joint Polar Satellite System (JPSS) Observing System Experiments (OSEs) Evaluating forecasts (examples) Evaluating JPSS OSE Classical Value added Summary 10
11 500 hpa Anomaly Correlation 11
12 500 hpa Anomaly Correlation But these are just width of the line improvements Why are these improvements important to users? Users don t live at 500 hpa, so what about sensible weather elements (precipitation, 2 m T, 2 m Td, max-min T)? 12
13 500 hpa Anomaly Correlation (Z500AC) These differences are statistically significant Can users see the difference? 13
14 Frequency Distribution of Z500AC Scores (annual average ) NH From one year to the next, changes may not be so apparent Over 5 years, changes are easily seen SH Changes are due to Observations System resolution* Data assimilation techniques* Model physics and dynamics* *Made possible by more computing power 14
15 Outline The Joint Polar Satellite System (JPSS) Observing System Experiments (OSEs) Evaluating forecasts (examples) Evaluating JPSS OSE Classical Value-added Summary 15
16 Z500AC Time Mean Results 5 Day (120 h) Average Scores CNTL: 87.5% NOPM: 87.0% Diff: 0.5% OPS: 87.7% CNTL: 85.1% NOPM: 83.8% Diff: 1.3% OPS: 85.2% 16
17 Z500AC Time History 17
18 What About Other Forecast Parameters? August SON DJF NH-Z500-AC NH-MSLP-AC NH-T-RMS-GRD NH-W-RMS-GRD CONUS PRC 24-48h CNTL vs NOPM CONUS PRC 60-84h CNTL better (Stat. Sig.) TR-W-RMS-GRD CNTL better (No Stat. Sig.) HUR-TRK-ATL N/A Neutral (No Stat. Sig.) HUR-TRK-EPAC N/A NOPM better (No Stat. Sig.) SH-Z500-AC NOPM better (Stat. Sig.) SH-MSLP-AC SH-T-RMS-GRD SH-W-RMS-GRD NH-W-RMS-OBS 24-48h SH-W-RMS-OBS 24-48h TR-W-RMS-OBS 24-48h NA-W-RMS-Obs 24-48h 18
19 Value-added Applying Frequency Distribution to OSE Results NH 00Z SH 00Z Fewer higher scores More lower scores NH 12Z SH 12Z 19
20 Quantitative Results Compare NOPM and OPS distributions with Control 20
21 Summary (1) OSE designed and executed to measure impact of loss of JPSS PM orbit data in future satellite configuration Loss of PM data degrades average Z500AC score by statistically significant values of 0.5% (NH) and 1.3% (SH) Consistent with other studies of its type Control slightly worse than OPS (not significant) 21
22 Summary (2) Compared to Control, NOPM is 13.6% more likely to produce forecast score in lowest 20%ile in NH 35.6% more likely in SH 11.9% less likely to produce forecast score in the upper 20%ile in NH 18.6% less likely in the SH Skewed toward producing more lower scores and fewer high scores Compared to Control, OPS is 13.6% less likely to produce score in lowest 20%ile in NH 10.2% less likely in SH More resilient system with a small redundancy that does not increase forecast accuracy but does reduce possibility of a 22 low score
23 Thanks Questions? 23
24 Observations Conventional Conventional Type of data Platform Instrument/Measurement Soundings Balloon Radiosonde Ship Radiosonde Pilot balloons (PIBALS) Wind Profiler Wind Commercial MDCRS Wind, temperature aircraft Canadian AMDAR Wind, temperature European AMDAR Wind, temperature Pilot reports Wind, temperature Land surface Airport surface data Surface meteorology ASOS and AWOS Surface meteorology Mesonets Surface meteorology Marine Ship Surface meteorology, SST Moored buoy Surface meteorology, SST Drifting buoy Surface meteorology, SST Hurricane Dropsondes Wind, temperature, moisture, pressure Military reconnaissance data Wind, temperature, moisture, pressure 24
25 Satellite Observations NOAA-19 Satellite NOAA Polar-Orbiting Environmental System (POES) AMSU-A HIRS MHS AVHRR SBUV EUMETSAT (Polarorbiting) NOAA-18 NOAA-17 NOAA-15 MetOp-A AMSU-A MHS SBUV HIRS SBUV AMSU-A IASI AMSU-A MHS HIRS GOME GRAS ASCAT NASA Aqua AIRS AMSU-A MODIS (AMVs) SNPP ATMS GNSSRO COSMIC-1 Radio Occultation TerraSAR-X Radio Occultation GRACE-A Radio Occultation SAC-C Radio Occultation CNOFS Radio Occultation NASA Aura OMI NOAA Geostationary Operational Environmental Satellite (GOES) GOES-13 GOES-15 Sounder Sounder Imager (AMVs) EUMETSAT METEOSAT-10 SEVIRI (Imager) SEVIRI (AMVs) METEOSAT-7 Imager (AMVs) JMA MTSAT-2 Imager (AMVs) 25
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