1 / 33

Chinnawat Surussavadee

Evaluation of High-Resolution Weather Forecasts in Tropics using Satellite Passive Millimeter-Wave Observations. Chinnawat Surussavadee. Andaman Environment and Natural Disaster Research Center (ANED) Faculty of Technology and Environment Prince of Songkla University, Phuket Campus, Thailand.

kamea
Download Presentation

Chinnawat Surussavadee

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Evaluation of High-Resolution Weather Forecasts in Tropics using Satellite Passive Millimeter-Wave Observations Chinnawat Surussavadee Andaman Environment and Natural Disaster Research Center (ANED) Faculty of Technology and Environment Prince of Songkla University, Phuket Campus, Thailand

  2. Introduction • Weather forecasting at high spatial resolution in tropics is challenging and is more difficult than that in extratropics • Most tropical clouds are formed by convective instabilities arising in minutes to hours with limited horizontal extent • Thailand is located in tropics and has often been affected by intense convective storms causing natural disasters • (Surussavadee and Staelin, 2006 and 2007) have shown agreement between MM5-simulated and AMSU-observed TBs over 122 globally distributed storms spanning a year NCEP/MM5/TBSCAT/F(λ) • This paper evaluates MM5 5-km forecasts for 79 storms spanning a year over Thailand and nearby regions at 8-12 hours in advance using coincident AMSU-observed TBs and AMSU MIT Precipitation retrieval products (AMP)

  3. MM5 Domain Configurations • 3 co-centered nested domains • NCEP GFS gridded analyses and forecasts every 3 hours were used as initial and boundary conditions • GFS data are at 0.5-degree lat/lon spatial resolution with 64 pressure levels from the ground to 0.27 mbar • Domain-3 forecasts at 5-km resolution 8 – 12 hours after initial time were evaluated. • Time difference between MM5 and AMSU is within 7.5 minutes

  4. 79 Representative Storm Systems 79 representative storm systems during December 2006 - November 2007 ; average size is ~ 950 km × 950 km

  5. Advanced Microwave Sounding Unit (AMSU) • Composed of two units • Aboard NOAA-15, -16, -17, -18, -19, and Metop-A 1. AMSU-A • + 15 channels • + 50-km resolution at nadir • + Primarily used for temperature sounding • 2. AMSU-B (MHS) • + 5 channels • + 15-km resolution at nadir • + Primarily used for humidity sounding AMSU-A AMSU-A and B AMSU-B Spectral Coverage: Zenith Optical Depth

  6. Computation of MM5 Forecasted TBs • Employs • 1) MM5 domain-3 forecasts at 5-km resolution • 2) Two-stream radiative transfer model, TBSCAT (Rosenkranz, 1998) • 3) Electromagnetic models, F(λ), for icy hydrometeors (Surussavadee and Staelin, 2006 and 2007) • 4) Atmospheric transmittance models (Liebe et al., 1992; Rosenkranz, 1998) • 5) Complex permittivities for water (Liebe, 1991) and ice (Hufford, 1991) • 6) Land emissivity ~ uniformly random from 0.91 to 0.97 • 7) Sea emissivity computed using FASTEM (English et al., 1998) • MM5-forecasted 5-km TBs were convolved with a Gaussian function with FWHM of 50 and 15 km to be compared with AMSU-A and –B, respectively

  7. AMP-3 Precipitation Retrieval Algorithm Inputs and Training for Step 3 of the Precipitation Rate Algorithm * *

  8. Examples of AMP-3 Retrievals 100°E 110°E 70°N 20°N N15 & N16 Annual Average Accumulation First Arctic precip. maps (pink is sea ice) 80°N 10°N July 15, 2003 Sep 29, 2006 Typhoon and ITCZ 2006 mm/h 0.25 0.5 1 mm/h 2 2.5 0.2 0.5 1 4 8 16 25 North Pole storm at 102-minute intervals N18 June 22, 2008 from 14:25 UTC to 22:56 UTC 80N 80N 30 100 300 1000 mm 3000 8000

  9. 2-yr Mean Annual Precipitation Error (Est. – Gauge)

  10. AMP-3, AMP-4, AMP-5, and GPCP vs. Gauge

  11. MM5 vs. AMSU TB Histogram Comparison AMSU-A ch. 1 AMSU-A ch. 3 Computed using 79 storms AMSU-A ch. 2 AMSU-A ch. 4 MM5 predicts too large ice particles

  12. MM5 vs. AMSU TB Histogram Comparison AMSU-A ch. 7 AMSU-A ch. 5 MM5 predicts too large ice particles AMSU-A ch. 8 AMSU-A ch. 6

  13. AMSU-B ch. 1 AMSU-B ch. 4 AMSU-B ch. 2 AMSU-B ch. 5 AMSU-B ch. 3

  14. August 6, 2007

  15. June 22, 2007

  16. August 20, 2007

  17. October 10, 2007

  18. November 13, 2007

  19. December 1, 2006

  20. Surface Precipitation Rate [mm/h] Snow Water-Path [mm] Graupel Water-Path [mm] Rain Water-Path [mm]

  21. Cloud Ice Water-Path [mm] Rain + Snow + Graupel Water-Path [mm] Peak Vertical Wind [m/s] Cloud Liquid Water [mm]

  22. MM5 vs. AMP Number of Raining Pixels Raining pixels: surface precipitation rate > 0.5 mm/h

  23. Summary and Conclusions • MM5 forecasts statistically agree with AMSU observations • Morphology, intensity, and area of storms forecasted by MM5 are generally similar to AMSU observations, but with location differences • MM5 over-forecasts large ice particles for some storms • MM5 can provide useful high-resolution forecasts for tropical storms ~8 hours in advance • Forecast accuracy could be improved by • + higher-resolution & more accurate initial and boundary conditions • + radar or satellite data for location correction • + a more accurate weather prediction model

  24. References [1] C. Surussavadee and D. H. Staelin, “Comparison of AMSU Millimeter-Wave Satellite Observations, MM5/TBSCAT Predicted Radiances, and Electromagnetic Models for Hydrometeors,” IEEE Trans. Geosci. Remote Sens., vol. 44, no. 10, pp. 2667-2678, Oct. 2006. [2] C. Surussavadee and D. H. Staelin, “Millimeter-Wave Precipitation Retrievals and Observed-versus-Simulated Radiance Distributions: Sensitivity to Assumptions,” J. Atmos. Sci., vol. 64, no. 11, pp. 3808-3826, Nov. 2007. [3] J. Dudhia, D. Gil, K. Manning, W. Wang, C. Bruyere, (2005, Jan.) PSU/NCAR Mesoscale Modeling System Tutorial Class Notes and Users’ Guide (MM5 Modeling System Version 3). [Online]. Available: http://www.mmm.ucar.edu/mm5/documents/tutorial-v3-notes.html [4] C. Surussavadee and D. H. Staelin, “Satellite retrievals of arctic and equatorial rain and snowfall rates using millimeter wavelengths,” IEEE Trans. Geosci. Remote Sens., vol. 47, no. 11, pp. 3697–3707, Nov. 2009. [5] C. Surussavadee and D. H. Staelin, “Global Precipitation Retrievals Using the NOAA/AMSU Millimeter-Wave Channels: Comparison with Rain Gauges,” J. Appl. Meteorol. Climatol., vol. 49, no. 1, pp. 124-135, Jan. 2010.

More Related