Satellite Wind Products

1. Satellite Wind Products Presented by Jaime Daniels

2. Requirement, Science, and Benefit Requirement/Objective • Mission Goal: Weather and Water • Research Area: Improve weather forecast and warning accuracy and amount of lead time • Mission Goal: Technology and the Mission Support • Research Area: Advancing space-based data collection capabilities and associated platforms and systems Science • How can we use polar imagers to provide wind information in the polar regions where conventional wind observations are scarce? • How can we improve the quality of satellite-derived winds and improve their utility and impact in Numerical Weather Prediction (NWP)? Benefit • Satellite derived wind products: • Provide vital tropospheric wind information over expansive regions of the earth devoid of in-situ wind observations that include oceans, polar regions, and Southern Hemisphere land masses. • Provide vital tropospheric wind information over low latitudes and on scales in higher latitudes where the geostrophic relationship is invalid • Provide key wind observations to operational NWP data assimilation systems where their use has been demonstrated to improved numerical weather prediction forecasts including tropical cyclones • Provide improved guidance for NWS field forecasters

3. Challenges and Path Forward • Science challenges • Satellite wind height assignment for optically thin clouds • Assignment of a height uncertainty with each satellite wind for the NWP community • Next steps • Development of a NPP VIIRS polar winds products (funded FY10 PSDI effort) • Complete development and validation of GOES-R satellite wind algorithm that includes new tracking algorithm approach (GOES-R AWG funded effort) • Apply GOES-R satellite wind algorithm approach for current operational GOES and polar instruments, but starting with the GOES instruments (funded FY10 PSDI effort) • Work with JCSDA and other NWP centers to assess impact of winds derived with new tracking algorithm on NWP forecast accuracy • Continued development of improved satellite winds validation tools that leverage use of new data sources (CALIPSO/CLOUDSAT, LIDAR winds) that will enable improved characterization of the accuracy and uncertainties associated with satellite derived winds • Transition Path • The GOES-R derived motion winds algorithm is scheduled to be delivered to the GOES-R system integrator by September 2010 • Work to apply the GOES-R derived motion winds algorithm to the current GOES series of satellites/instruments is scheduled to begin June 2010 (a PSDI funded effort). End goal of effort is to replace the current operational derived motion winds algorithm by March 2012 • Transition of VIIRS polar wind products to operations to begin late 2011 (a PSDI funded effort)

4. Basics of Satellite Winds Derivation Visible (0.64um) Visible (0.64um) SWIR (3.9um) SWIR (3.9um) Mid-IR (6.7um) Mid-IR (6.7um) LWIR (11um) LWIR (11um) • Atmospheric motion is determined through the tracking of features (clouds or moisture gradients) in time • The choice of spectral band determines the intended target and location (low, mid, upper troposphere) in the atmosphere • Use a pattern matching algorithm for estimating motion of features • Sum-of-Squared Differences (SSD) • Use multi-spectral height assignment algorithms to assign heights to features being tracked • Multi-spectral approaches: CO2 slicing, H2O-intercept, Histogram algorithms • Clear-sky radiances per a forward Radiative Transfer Model (RTM) • Atmospheric state per NWP forecasts • Apply quality control • NWP forecast to flag outliers • Internal consistency checks • Compute and assign product quality indicators • QI approach • Error Estimation (EE) approach Visible Cloud-drift Winds - Daytime - Lower troposphere Short-wave IR Cloud-drift Winds - Night-time - Lower troposphere Water Vapor Winds - Cloud-top - Clear-sky - Mid to Upper troposphere Long-wave IR Cloud-drift Winds - Day and night - Lower, mid, and upper troposphere

5. Satellite Winds Research • Development of polar wind products • Motivation: Provide satellite wind observations over polar regions where conventional in-situ wind observations are lacking • Improving the refresh rate of geostationary wind products • Motivation: Provide more frequent satellite winds for use in emerging operational 4D-VAR data assimilation systems at NWP centers • Development of a new and novel tracking algorithm • Motivation: Address and minimize the long standing problem of the observed slow speed bias associated with mid and upper-level satellite-derived winds; a significant concern of NWP community • Development of new approaches and tools to validate satellite wind height assignments • Motivation: Quantify the height uncertainty of satellite winds, improve their accuracy, and improve their use in NWP

6. Polar Wind Product Innovations Terra only or Aqua only Aqua, Terra, Aqua Benefits • Provide unprecedented coverage of the polar wind field that improves polar wind analyses • Continuity: Recent use of AVHRR for polar wind estimation prepares us for a future without MODIS • Demonstrated positive forecast impacts • Medium range weather forecasts, not just over the polar regions, but globally • Reduction in the frequency of forecast busts • Reduction in tropical storm track forecast errors MODIS Winds AVHRR Winds Single Satellite (Aqua or Terra) NOAA-AVHRR GAC Winds Mixed Satellite (Aqua and Terra) METOP-AVHRR Winds

7. Innovation: Improving the Refresh Rateof Geostationary Wind Products Benefits • Improve refresh rate of GOES-E/W wind products from 3-hourly to hourly • Provide a more continuous (in time) source of satellite wind observations for emerging operational 4D-VAR NWP data assimilation systems • Potential for significant and positive impacts on NWP forecast accuracy GOES-12 Hourly Cloud-drift Winds GOES-11 Hourly Cloud-drift Winds

8. Feature Tracking Algorithm Innovations 10 5 m/s 0 -5 Date Before clustering After clustering New Nested Tracking Algorithm • Developed for future GOES-R ABI • Aims to minimize observed slow speed bias of satellite winds; a significant concern for NWP • Computes local motions (nested) within a larger target scene, together with a clustering algorithm, to arrive at a superior motion solution • Potential for determination of motion at different levels and/or different scales Sat vs. Rawinsonde (100-400 hPa) Mean Vector Difference 1–2 m/s slow bias Speed Bias Nested Tracking 5 Lines Motion of entire box SPD: 22.3 m/s Average of largest cluster SPD: 27.6 m/s 5 Elements 15 Lines 15 Elements

9. Feature Tracking Algorithm Innovations Comparisons to Rawinsondes Test winds are better fit to radiosonde winds Black - control Light Blue -test AMV Speed (m/s) RAOB Speed (m/s) Benefits • Improved wind estimates • Near elimination of slow speed bias • Reduction of vector RMS error • Potential for significant and positive impacts on NWP forecast accuracy • Impact studies with JCSDA planned Winds generated using Meteosat-8 10.8 μm imagery (15-minute loop interval) for the period Feb 1 - 28, 2008.

10. Innovations in Satellite Wind Height Assignment Validation CALIPSO Cloud Height Satellite Wind Height Benefits • Leverages unprecedented cloud information offered by CALIPSO and CloudSat measurements • Enables improved error characterization of satellite wind height assignments • Enables feedback for potential improvements to satellite wind height assignments • Improvements to overall accuracy of satellite-derived winds Using CALIPSO/CloudSat Data to Validate Satellite Wind Height Assignments GOES-12 Cloud-drift Wind Heights Overlaid on CALIPSO total attenuated backscatter image at 532nm

11. Challenges and Path Forward • Science challenges • Satellite wind height assignment for optically thin clouds • Assignment of a height uncertainty with each satellite wind for the NWP community • Next steps • Development of a NPP VIIRS polar winds products (funded FY10 PSDI effort) • Complete development and validation of GOES-R satellite wind algorithm that includes new tracking algorithm approach (GOES-R AWG funded effort) • Apply GOES-R satellite wind algorithm approach for current operational GOES and polar instruments, but starting with the GOES instruments (funded FY10 PSDI effort) • Work with JCSDA and other NWP centers to assess impact of winds derived with new tracking algorithm on NWP forecast accuracy • Continued development of improved satellite winds validation tools that leverage use of new data sources (CALIPSO/CLOUDSAT, LIDAR winds) that will enable improved characterization of the accuracy and uncertainties associated with satellite derived winds • Transition Path • The GOES-R derived motion winds algorithm is scheduled to be delivered to the GOES-R system integrator by September 2010 • Work to apply the GOES-R derived motion winds algorithm to the current GOES series of satellites/instruments is scheduled to begin June 2010 (a PSDI funded effort). End goal of effort is to replace the current operational derived motion winds algorithm by March 2012 • Transition of VIIRS polar wind products to operations to begin late 2011 (a PSDI funded effort)