Chinnawat Surussavadee

Faculty of Technology and Environment Prince of Songkla University 1www.aned.psu.ac.th

Chinnawat Surussavadee

July 2011

Evaluation of High-Resolution Weather Forecasts in Tropics using Satellite Passive

Millimeter-Wave Observations


Andaman Environment and Natural Disaster Research Center (ANED)Faculty of Technology and Environment

Prince of Songkla University, Phuket Campus, Thailand



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 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)

Introduction



July 2011

MM5 Domain Configurations

Domain Number of cells

Cell size (km)

Implicit scheme

Explicit scheme

Time step (sec)

1 100*100 45 KF2 Goddard 402 190*190 15 KF2 Goddard 13.333 190*190 5 None Goddard 4.44

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



July 2011

79 Representative Storm Systems

79 representative storm systems during December 2006 - November 2007 ; average size is ~ 950 km × 950 km



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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



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 TBs



July 2011

Inputs and Training for Step 3 of the Precipitation Rate Algorithm

**

AMP-3 Precipitation Retrieval Algorithm

Case PCA inputs

PCA training NN Inputs NN training

A Land A4-8 Land; 122 orbits PC1, B3-4 106 MM5, landB Sea |lat|<45

53.6 GHz ≥ 248KA1-8, B1-5 Ice-free sea

122 AMSU orbitsPC2-5 106 MM5

ice-free seaC All sea pixels

that are not BA4-8 Sea 53.6 < 248K

122 AMSU orbitsPC1-2, B3-4 106 MM5, sea

53.6 GHz < 248KD All sea A4-8 Same as C Same as C 106 MM5, sea



July 2011

Examples of AMP-3 Retrievals

July 15, 2003

70°N

80°N

10.25 2mm/h0.5 2.5

First Arctic precip. maps

(pink is sea ice)

80N 80N

North Pole storm at 102-minute intervalsN18 June 22, 2008 from 14:25 UTC to 22:56 UTC

100030 8000mm100 300 3000

N15 & N16 Annual Average Accumulation

2006

100°E

110°E20°

N

10°N

Sep 29, 2006

40.2 16 25mm/h0.5 1 8

Typhoon and ITCZ



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



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



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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



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



July 2011

AMSU-B ch. 1

AMSU-B ch. 2

AMSU-B ch. 4

AMSU-B ch. 3

AMSU-B ch. 5



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August 6, 2007



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June 22, 2007



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August 20, 2007



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October 10, 2007



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November 13, 2007



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December 1, 2006



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Surface Precipitation Rate [mm/h]

Rain Water-Path [mm]

Snow Water-Path [mm]

Graupel Water-Path [mm]



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Rain + Snow + Graupel Water-Path [mm]

Cloud Liquid Water [mm]

Cloud Ice Water-Path [mm]

Peak Vertical Wind [m/s]



July 2011

MM5 vs. AMP Number of Raining Pixels

Raining pixels: surface precipitation rate > 0.5 mm/h



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



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



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Documents

Chinnawat Surussavadee