Presentation is loading. Please wait.

Presentation is loading. Please wait.

Faculty of Technology and Environment Prince of Songkla University 1 www.aned.psu.ac.th Chinnawat Surussavadee July 2011 Evaluation of High-Resolution.

Published byLydia Wilcox Modified over 8 years ago

Similar presentations

Presentation on theme: "Faculty of Technology and Environment Prince of Songkla University 1 www.aned.psu.ac.th Chinnawat Surussavadee July 2011 Evaluation of High-Resolution."— Presentation transcript:

1 Faculty of Technology and Environment Prince of Songkla University 1 www.aned.psu.ac.th Chinnawat Surussavadee July 2011 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 Faculty of Technology and Environment Prince of Songkla University 2 www.aned.psu.ac.th Chinnawat Surussavadee July 2011  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 T B s 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 T B s and AMSU MIT Precipitation retrieval products (AMP) Introduction

3 Faculty of Technology and Environment Prince of Songkla University 3 www.aned.psu.ac.th Chinnawat Surussavadee July 2011 MM5 Domain Configurations Domain Number of cells Cell size (km) Implicit scheme Explicit scheme Time step (sec) 1100*10045KF2Goddard40 2190*19015KF2Goddard13.33 3190*1905NoneGoddard4.44  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 Faculty of Technology and Environment Prince of Songkla University 4 www.aned.psu.ac.th Chinnawat Surussavadee July 2011 79 Representative Storm Systems 79 representative storm systems during December 2006 - November 2007 ; average size is ~ 950 km × 950 km

5 Faculty of Technology and Environment Prince of Songkla University 5 www.aned.psu.ac.th Chinnawat Surussavadee July 2011 Advanced Microwave Sounding Unit (AMSU) AMSU-A AMSU-BAMSU-A and B  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 Spectral Coverage: Zenith Optical Depth

6 Faculty of Technology and Environment Prince of Songkla University 6 www.aned.psu.ac.th Chinnawat Surussavadee July 2011  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 Computation of MM5 Forecasted T B s

7 Faculty of Technology and Environment Prince of Songkla University 7 www.aned.psu.ac.th Chinnawat Surussavadee July 2011 Inputs and Training for Step 3 of the Precipitation Rate Algorithm * * AMP-3 Precipitation Retrieval Algorithm Case PCA inputsPCA training NN InputsNN training A LandA4-8Land; 122 orbitsPC1, B3-4106 MM5, land B Sea |lat|<45 53.6 GHz ≥ 248K A1-8, B1-5Ice-free sea 122 AMSU orbits PC2-5106 MM5 ice-free sea C All sea pixels that are not B A4-8Sea 53.6 < 248K 122 AMSU orbits PC1-2, B3-4106 MM5, sea 53.6 GHz < 248K D All seaA4-8Same as C 106 MM5, sea

8 Faculty of Technology and Environment Prince of Songkla University 8 www.aned.psu.ac.th Chinnawat Surussavadee July 2011 Examples of AMP-3 Retrievals July 15, 2003 70°N 80°N 10.252mm/h0.5 2.5 First Arctic precip. maps (pink is sea ice) 80N North Pole storm at 102-minute intervals N18 June 22, 2008 from 14:25 UTC to 22:56 UTC 1000308000mm1003003000 N15 & N16 Annual Average Accumulation 2006 100 °E 110° E 20° N 10° N Sep 29, 2006 40.21625 mm/h 0.518 Typhoon and ITCZ

9 Faculty of Technology and Environment Prince of Songkla University 9 www.aned.psu.ac.th Chinnawat Surussavadee July 2011 2-yr Mean Annual Precipitation Error (Est. – Gauge)

10 Faculty of Technology and Environment Prince of Songkla University 10 www.aned.psu.ac.th Chinnawat Surussavadee July 2011 AMP-3, AMP-4, AMP-5, and GPCP vs. Gauge

11 Faculty of Technology and Environment Prince of Songkla University 11 www.aned.psu.ac.th Chinnawat Surussavadee July 2011

12 Faculty of Technology and Environment Prince of Songkla University 12 www.aned.psu.ac.th Chinnawat Surussavadee July 2011

13 Faculty of Technology and Environment Prince of Songkla University 13 www.aned.psu.ac.th Chinnawat Surussavadee July 2011 AMSU-A ch. 1 AMSU-A ch. 2 AMSU-A ch. 3 AMSU-A ch. 4 MM5 predicts too large ice particles MM5 vs. AMSU TB Histogram Comparison Computed using 79 storms

14 Faculty of Technology and Environment Prince of Songkla University 14 www.aned.psu.ac.th Chinnawat Surussavadee July 2011 AMSU-A ch. 5 AMSU-A ch. 6 AMSU-A ch. 7 AMSU-A ch. 8 MM5 predicts too large ice particles MM5 vs. AMSU TB Histogram Comparison

15 Faculty of Technology and Environment Prince of Songkla University 15 www.aned.psu.ac.th Chinnawat Surussavadee July 2011 AMSU-B ch. 1 AMSU-B ch. 2 AMSU-B ch. 4 AMSU-B ch. 3 AMSU-B ch. 5

16 Faculty of Technology and Environment Prince of Songkla University 16 www.aned.psu.ac.th Chinnawat Surussavadee July 2011 August 6, 2007

17 Faculty of Technology and Environment Prince of Songkla University 17 www.aned.psu.ac.th Chinnawat Surussavadee July 2011 June 22, 2007

18 Faculty of Technology and Environment Prince of Songkla University 18 www.aned.psu.ac.th Chinnawat Surussavadee July 2011 August 20, 2007

19 Faculty of Technology and Environment Prince of Songkla University 19 www.aned.psu.ac.th Chinnawat Surussavadee July 2011 October 10, 2007

20 Faculty of Technology and Environment Prince of Songkla University 20 www.aned.psu.ac.th Chinnawat Surussavadee July 2011 November 13, 2007

21 Faculty of Technology and Environment Prince of Songkla University 21 www.aned.psu.ac.th Chinnawat Surussavadee July 2011 December 1, 2006

22 Faculty of Technology and Environment Prince of Songkla University 22 www.aned.psu.ac.th Chinnawat Surussavadee July 2011 Surface Precipitation Rate [mm/h] Rain Water-Path [mm] Snow Water-Path [mm] Graupel Water-Path [mm]

23 Faculty of Technology and Environment Prince of Songkla University 23 www.aned.psu.ac.th Chinnawat Surussavadee July 2011 Rain + Snow + Graupel Water-Path [mm] Cloud Liquid Water [mm] Cloud Ice Water-Path [mm] Peak Vertical Wind [m/s]

24 Faculty of Technology and Environment Prince of Songkla University 24 www.aned.psu.ac.th Chinnawat Surussavadee July 2011 MM5 vs. AMP Number of Raining Pixels Raining pixels: surface precipitation rate > 0.5 mm/h

25 Faculty of Technology and Environment Prince of Songkla University 25 www.aned.psu.ac.th Chinnawat Surussavadee July 2011  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 Summary and Conclusions

26 Faculty of Technology and Environment Prince of Songkla University 26 www.aned.psu.ac.th Chinnawat Surussavadee July 2011 [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. References

27 Faculty of Technology and Environment Prince of Songkla University 27 www.aned.psu.ac.th Chinnawat Surussavadee July 2011

28 Faculty of Technology and Environment Prince of Songkla University 28 www.aned.psu.ac.th Chinnawat Surussavadee July 2011

29 Faculty of Technology and Environment Prince of Songkla University 29 www.aned.psu.ac.th Chinnawat Surussavadee July 2011

30 Faculty of Technology and Environment Prince of Songkla University 30 www.aned.psu.ac.th Chinnawat Surussavadee July 2011

31 Faculty of Technology and Environment Prince of Songkla University 31 www.aned.psu.ac.th Chinnawat Surussavadee July 2011

32 Faculty of Technology and Environment Prince of Songkla University 32 www.aned.psu.ac.th Chinnawat Surussavadee July 2011

33 Faculty of Technology and Environment Prince of Songkla University 33 www.aned.psu.ac.th Chinnawat Surussavadee July 2011

Download ppt "Faculty of Technology and Environment Prince of Songkla University 1 www.aned.psu.ac.th Chinnawat Surussavadee July 2011 Evaluation of High-Resolution."

Similar presentations

Introduction to data assimilation in meteorology Pierre Brousseau, Ludovic Auger ATMO 08,Alghero, 15-18 september 2008.

Introduction to data assimilation in meteorology Pierre Brousseau, Ludovic Auger ATMO 08,Alghero, september 2008.
Precipitation Products PPS Anke Thoss, SMHI User Workshop, February 2015, Madrid.

Precipitation Products PPS Anke Thoss, SMHI User Workshop, February 2015, Madrid.
Allison Parker Remote Sensing of the Oceans and Atmosphere.

Allison Parker Remote Sensing of the Oceans and Atmosphere.
1 FWC 2006-10-24 IPWG MIT Lincoln Laboratory * This work was sponsored by the National Aeronautics and Space Administration under Contract NNG 04HZ53C,

1 FWC IPWG MIT Lincoln Laboratory * This work was sponsored by the National Aeronautics and Space Administration under Contract NNG 04HZ53C,
TRMM Tropical Rainfall Measurement (Mission). Why TRMM? n Tropical Rainfall Measuring Mission (TRMM) is a joint US-Japan study initiated in 1997 to study.

TRMM Tropical Rainfall Measurement (Mission). Why TRMM? n Tropical Rainfall Measuring Mission (TRMM) is a joint US-Japan study initiated in 1997 to study.
Improved Simulations of Clouds and Precipitation Using WRF-GSI Zhengqing Ye and Zhijin Li NASA-JPL/UCLA June, 2011.

Improved Simulations of Clouds and Precipitation Using WRF-GSI Zhengqing Ye and Zhijin Li NASA-JPL/UCLA June, 2011.
MICROWAVE RAINFALL RETRIEVALS AND VALIDATIONS R.M. GAIROLA, S. POHREL & A.K. VARMA OSD/MOG SAC/ISRO AHMEDABAD.

MICROWAVE RAINFALL RETRIEVALS AND VALIDATIONS R.M. GAIROLA, S. POHREL & A.K. VARMA OSD/MOG SAC/ISRO AHMEDABAD.
Microwave Imagery and Tropical Cyclones Satellite remote sensing important resource for monitoring TCs, especially in data sparse regions Passive microwave.

Microwave Imagery and Tropical Cyclones Satellite remote sensing important resource for monitoring TCs, especially in data sparse regions Passive microwave.
Climate modeling Current state of climate knowledge – What does the historical data (temperature, CO 2, etc) tell us – What are trends in the current observational.

Climate modeling Current state of climate knowledge – What does the historical data (temperature, CO 2, etc) tell us – What are trends in the current observational.
Passive Microwave Rain Rate Remote Sensing Christopher D. Elvidge, Ph.D. NOAA-NESDIS National Geophysical Data Center E/GC2 325 Broadway, Boulder, Colorado.

Passive Microwave Rain Rate Remote Sensing Christopher D. Elvidge, Ph.D. NOAA-NESDIS National Geophysical Data Center E/GC2 325 Broadway, Boulder, Colorado.
Hector simulation We found simulation largely depending on: Model initialization scheme Lateral boundary conditions Physical processes represented in the.

Hector simulation We found simulation largely depending on: Model initialization scheme Lateral boundary conditions Physical processes represented in the.
An artificial neural networks system is used as model to estimate precipitation at 0.25° x 0.25° resolution. Two different networks are being developed,

An artificial neural networks system is used as model to estimate precipitation at 0.25° x 0.25° resolution. Two different networks are being developed,
1 st UNSTABLE Science Workshop 18-19 April 2007 Science Question 3: Science Question 3: Numerical Weather Prediction Aspects of Forecasting Alberta Thunderstorms.

1 st UNSTABLE Science Workshop April 2007 Science Question 3: Science Question 3: Numerical Weather Prediction Aspects of Forecasting Alberta Thunderstorms.
MIT Lincoln Laboratory MIS IGARSS11-1 RVL 9/15/2010 An Atmospheric Algorithm Suite Based on Neural Networks for Microwave Imager/Sounders W. J. Blackwell,

MIT Lincoln Laboratory MIS IGARSS11-1 RVL 9/15/2010 An Atmospheric Algorithm Suite Based on Neural Networks for Microwave Imager/Sounders W. J. Blackwell,
UNDERSTANDING TYPHOONS

UNDERSTANDING TYPHOONS
ECMWF – 1© European Centre for Medium-Range Weather Forecasts Developments in the use of AMSU-A, ATMS and HIRS data at ECMWF Heather Lawrence, first-year.

ECMWF – 1© European Centre for Medium-Range Weather Forecasts Developments in the use of AMSU-A, ATMS and HIRS data at ECMWF Heather Lawrence, first-year.
Data assimilation of polar orbiting satellites at ECMWF

Data assimilation of polar orbiting satellites at ECMWF
Ten Gars Stefania De Angelis

Ten Gars Stefania De Angelis
Use of Humidity data from MT and other platforms for Science projects on Monsoon Cloud systems KUSUMA G RAO Space Sciences Indian Space Research Organization.

Use of Humidity data from MT and other platforms for Science projects on Monsoon Cloud systems KUSUMA G RAO Space Sciences Indian Space Research Organization.
Forecasting and Numerical Weather Prediction (NWP) NOWcasting Description of atmospheric models Specific Models Types of variables and how to determine.

Forecasting and Numerical Weather Prediction (NWP) NOWcasting Description of atmospheric models Specific Models Types of variables and how to determine.

Similar presentations

Ads by Google