a physically-based rainfall rate algorithm for all

A Physically-based Rainfall Rate Algorithm for All Surfaces: Applicability to All Microwave

Sensors Including TRMM & GPM

Sid-Ahmed Boukabara1

Kevin Garrett2, Leslie Moy2, Flavio Iturbide-Sanchez2, Chris Grassotti2 and Wanchun Chen2

2010 PMM Science Team Meeting Seattle, WA

November 1-4, 2010

1. NOAA/NESDIS/STAR, JCSDA, 2. IMSG Inc.

Agenda

 Overview: Microwave Integrated Retrieval System  MiRS Approach to Rainfall retrieval over all surfaces  MiRS Current Performance for Rainfall Rate  MiRS Extension to TMI & GPM/GMI (Proxy Data)  Current Limitations and Upcoming Improvements  MiRS Contribution to Sfc Emissivity characterization  Summary

MiRS Algorithm Description

RR = c0 + c1RWP+c2IWP+c3CLW

Rainfall Rate •  Rainfall rate based on relationship to integrated hydrometeor values (not sensor-specific Tbs) •  Extension to other sensors does not require regenerating other coefficients •  Coefficients derived from MM5 simulations •  Same approach used over ocean and land

Vertical Integration and Post-Processing 1D

VAR

O

utpu

ts

Vertical Integration

Post Processing

(Algorithms)

TPW RWP IWP CLW

Core Products

Temp. Profile

Humidity Profile

Emissivity Spectrum

Skin Temperature

Liq. Amount Prof

Ice. Amount Prof

Rain Amount Prof

-Sea Ice Concentration -Snow Water Equivalent -Snow Pack Properties -Land Moisture/Wetness -Rain Rate -Snow Fall Rate -Wind Speed/Vector -Cloud Top -Cloud Thickness -Cloud phase

4

Simultaneous Retrieval?

X is the solution

F(X) Fits Ym within Noise levels

X is a solution

Necessary Condition (but not sufficient)

If X is the set of parameters that impact the radiances Ym, and F the Fwd Operator

If F(X) Does not Fit Ym within Noise

X is not the solution

All parameters are retrieved simultaneously to fit all radiances together

Suggests it is not recommended to use independent algorithms for different parameters, since they don’t guarantee the fit to the radiances

5

Solution-Reaching: Convergence   Convergence is reached everywhere: all surfaces, all weather

conditions including precipitating, icy conditions   A radiometric solution (whole state vector) is found even when

precip/ice present. With CRTM physical constraints.

Previous version (non convergence when precip/ice present)

Current version

MiRS is applied to a number of microwave sensors, each time gaining robustness and improving validation for Future New Sensors •  The exact same executable, forward operator, covariance matrix used for all sensors •  Modular design •  Cumulative validation and consolidation of MiRS

POES N18/N19

√

DMSP SSMIS

F16/F18

√

AQUA AMSR-E

√

NPP/JPSS ATMS

√ √: Applied Operationally

√: Applied occasionally

√: Tested in Simulation

Metop-A

√

TRMM/GPM/ M-T

TMI, GMI proxy, SAPHIR/MADRAS

√

Current & Planned Capabilities

7

MiRS Approach to Rainfall over All surfaces

Case area after rain event

CPC Figures courtesy http://www.cpc.necp.noaa.gov

CPC real-time 24-hour precipitation from 12Z 2010-10-19, 2010-10-20, 2010-10-22 and 2010-10-23 (from left to right)

MiRS N18 retrieved emissivity at 31 GHz ascending node for 2010-10-19, 2010-10-20, 2010-10-22 and 2010-10-23 (from left to right)

Day in October

Es 19.35V channel 37.0 V channel

Illustration of High Variability of Emissivity (1/2)

Compare 2 days before rain event: 2010-10-19 and 2010-10-20

Compare 1 day before with 1 day after: 2010-10-19 and 2010-10-23

There is a significant difference between emissivities before rain events and during (or shortly after) rain events

Using pre-determined emissivities (monthly, weekly or even daily updates) could potentially lead to issues:

For example: Low emiss values after rain could be interpreted as false rain

Bias:0.00 StdDev:0.01 Bias:0.02 StdDev:0.02

Illustration of High Variability of Emissivity (2/2)

MiRS Approach to handling RR over all surfaces

 Emissivity is to be considered highly variable, especially when rain is present, or when it rained a few hours/days ago.

  It is also highly variable on a footprint-by-footprint level, especially in heterogeneous areas (rivers, coasts, mountains, etc)

 MiRS approach is to include emissivity as part of the state vector, along with hydrometeor

 Rely on physical constraints and forward operator/Jacobian to distinguish emisssivity and hydrometeor signals

10

MiRS TPW Weekly composite

MiRS Emiss Weekly composite

Ex. of MiRS emissivity capturing heterogeneous signal (allowing a smooth retrieval of other products along the coast line)

11

Added Value of Emissivity Handling: Same RR algorithm Over Both Ocean and Land

Image taken from IPWG web site: credit to Daniel Villa

No discontinuity at coasts (MiRS applies to both land and ocean)

12

MiRS Current Performance for Rainfall Rate

Independent Validation (IPWG) 1/2

 Provide MiRS PE over CONUS, S.A. and Australia

  Independent assessment versus radars/gauges

 Comparison to other precipitation algorithms

Images courtesy of John Janowiak, UMD; Daniel Vila,

CPTEC; and Elizabeth Ebert,

Australian Bureau of Meteorology

Independent Validation (IPWG) 2/2

 Monitor a running time series of statistics relative to rain gauges

 Intercomparison with other PE algorithms and radar

Caution: algorithms perfs depend on how many sensors are used

MiRS Testbed: Climatology & Vertical Structure

MiRS Hydrometeor Profiles

TRMM 2A12 Hydrometeor Profiles

Hydrometeor Profiles/Vertical Cross Sections

Rainfall Climatologies

Monthly Averaged MSPPS NOAA-18 Rainfall Rate for 11-2009

Monthly Averaged MiRS NOAA-18 Rainfall Rate for 2009

MiRS

Heritage

16

MiRS Extension to TRMM/TMI and GPM/GMI

Work initiated a month ago: in progress. These are draft results

MiRS for TRMM/TMI

Example of retrieved rainfall rate from MiRS on TMI data at ~5 km resolution (left) compared to TRMM 2A12 (right) for 2010-09-19

MiRS has been extended to TRMM/TMI (work still in progress)

Current issues being addressed: - Non-convergence

- Coastal false alarm signal

Extension of MiRS to GPM/GMI (1/2)

18

  GPM/GMI proxy data (simulated brightness temperatures) were generated to test MiRS algorithm.

  Simulations performed using CRTM forward model and ECMWF geophysical inputs

  Simulations over all surfaces   TRMM/TMI metadata used (for

scanning geometry, angle, swath, time, etc) and also for emissivity

  Simulations performed daily at NOAA.

  Goal: Make sure the algorithm is ready on day-1 for GPM/GMI data (switch between proxy data flow and real data stream)

Example: GMI simulated 36.5 GHz H-pol TB

Current issue being addressed: Apparent pixel shift

GMI is similar to TMI with additional high frequency channels (166 and 183 GHz)

We look forward to using L1B data from GPM simulator (Matsui et al)

Extension of MiRS to GPM/GMI (2/2)

19

MiRS has been applied on the GPM/GMI Proxy data. All products are being assessed, including RR, Emissivity, TPW, etc

Draft Results: Work is still in progress to optimize the emissivity covariance for GMI and TMI

GMI Emiss @ 36.5 GHz H-pol

GMI TPW

Current Limitations & Planned Improvements

Sensor Applicability

Current Limitations Planned Improvements

Importance/ Difficulty

All sensors Current atmospheric covariance is a single covariance used globally

Current effort aims at developing stratified covariances, by latitude and season

Important (to improve warm season perfs)

All sensors Rain Rate relationship (w 1DVAR hydrometeors) is also a single relationship, used globally

Investigate the stratification of rainrate relationship by season/latitude

Important (to improve warm season perfs)

All sensors Very low false alarm rate but Low detection Rate, especially for light rain, due to compensation of light rain signal by other parameters (such as WV)

Make sure high frequency channels have a stronger weight in the Chi-Square computation

Moderate

TMI/GMI Important coastal False Alarm

Improve emissivity covariance (not mature yet for these sensors)

Low

21

MiRS Contribution to Surface Emissivity Characterization

Global Variationally-based Inversion of Emissivity: Routine Assessment

22

MiRS inverts emissivities for all channels, including high-frequency (Inversion performed in EOF space)

Emissivity is assessed by comparing it to analytically-inverted emissivity

Surface Emissivity Inter-Comparison 12/01/2007 – 02/28/2009

Frequency (GHz)

Es

Es

MiRS N18

GDAS

MiRS N18 minus GDAS

Emis

sivi

ty d

iffer

ence

(MiR

S-A

naly

t)

Frequency (GHz)

Ocean __ Sea Ice (Antartic) ___ Sea Ice (Arctic) ___ Sea Ice (First Year) ___

Desert __ Amazon __ Wet Land __ Snow __

Intercomparison between MiRS variational emissivities and analytical ones

Differences within 2%. Larger diffs noticed for snow (~8%) & Arctic sea-ice (3%). Questions: Tskin used in analytical emiss from GDAS accurate enough?

Is assumption of specularity valid for snow and sea-ice?

MiRS TRMM Emissivities (in clear and rainy conditions)

Frequency (GHz)

Non Precip., vertical polar.

horizontal polarization

Rainy, vertical polar.

horizontal polarization

Non Precip & Rainy

Aver

aged

Em

issi

vitie

s us

ing

over

land

Rain changes dramatically the emissivities: -  Water-type spectral shape -  Lower values -  Higher V-H contrast

Summary   MiRS is a variational algorithm (1DVAR) and can be applied to virtually any

microwave sensor   MiRS uses CRTM as forward and jacobian operators   Retrieves sounding & surface parameters simultaneously, including

hydrometeor profiles, rain rate & surface emissivity   Applicable over all surfaces (emissivity is part of the state vector), allowing

a spot-by-spot variability of the surface emissivity.   Extensively assessed both internally and independently.   Applicability in all-weather conditions (including rainy)   Run operationally at NOAA for N18, N19, SSMIS F16, F18 and Metop-A,

and being integrated for NPP/JPSS ATMS   MiRS is also currently being extended to support GPM (GMI) and Megha-

Tropiques (MADRAS and SAPHIR)   Current enhancements to the algorithm expected to improve performances

of hydrometeor retrievals for all sensors   We look forward to using GV data when they become available (plan to

extend CRTM, and therefore MiRS to airborne setups) and GPM simulator.   Variational Emissivities from MiRS are available (all surfaces, for all

frequencies) as well as corresponding covariances. MiRS is a community algorithm (available publicly), benefiting from

community-driven improvements, suggestions, scrutiny and assessment.

26

BACKUP SECTION

27

1D-Variational Retrieval/Assimilation

MiRS Algorithm

Measured Radiances

Initi

al S

tate

Vec

tor

Solution Reached

Forward Operator (CRTM)

Simulated Radiances Comparison: Fit

Within Noise Level ?

Update State Vector

New State Vector

Yes

No Jacobians

Geophysical Covariance

Matrix B

Measurement & RTM

Uncertainty Matrix E

Geophysical Mean

Background

Climatology (Retrieval Mode) Forecast Field (1D-Assimilation Mode)

MiRS is a physical algorithm:

Solution constrained by

(1) geophysical covariance, (2) fitting measurements, (3) physical Jacobians, (4) physical radiative transfer and (5) simultaneous retrieval of all parameters

28

MiRS General Overview

Radiances

Rapid Algorithms

(Regression)

Advanced Retrieval (1DVAR)

Vertical Integration &

Post-processing

selection

1st Guess

MIRS Products

Vertical Integration and Post-Processing 1D

VAR

O

utpu

ts

Vertical Integration

Post Processing

(Algorithms)

TPW RWP IWP CLW

Core Products

Temp. Profile

Humidity Profile

Emissivity Spectrum

Skin Temperature

Liq. Amount Prof

Ice. Amount Prof

Rain Amount Prof

-Sea Ice Concentration -Snow Water Equivalent -Snow Pack Properties -Land Moisture/Wetness -Rain Rate -Snow Fall Rate -Wind Speed/Vector -Cloud Top -Cloud Thickness -Cloud phase

29

All-Weather and All-Surfaces

Scattering Effect

Scattering Effect

Absorption

Surface

sensor Major Parameters for RT: •  Sensing Frequency •  Absorption and scattering properties of material •  Geometry of material/wavelength interaction •  Vertical Distribution •  Temperature of absorbing layers •  Pressure at which wavelength/absorber interaction occurs •  Amount of absorbent(s) •  Shape, diameter, phase, mixture of scatterers.

Sounding Retrieval: •  Temperature •  Moisture

  Instead of guessing and then removing the impact of cloud and rain and ice on TBs (very hard), MiRS approach is to account for cloud, rain and ice within its state vector.

  It is highly non-linear way of using cloud/rain/ice-impacted radiances.

To account for cloud, rain, ice, we add the following in the state vector: •  Cloud (non-precipitating) •  Liquid Precipitation •  Frozen precipitation

To handle surface-sensitive channels, we add the following in the state vector: •  Skin temperature •  Surface emissivity (proxy parameter for all surface parameters)

MiRS TMI Surface Emissivity- channel libraries can be built

85.5 V 19.35 H

37.0 H

10.65H

19.35V

37.0V 85.5H

21.3H 10.65V

MiRS F18 SSMI/S Oct. 18, RR mm/hr



MiRS F18 SSMI/S Oct. 18, 37V Emis

MiRS F18 SSMI/S RR Oct. 23, 37V Emis

MiRS F18 SSMI/S RR Oct. 27, 37V Emis

October

Es 19.35V channel 37.0 V channel

18th 23rd 27th

Surface Emissivity – Rain Event

MiRS F18 SSMI/S (RPW > 0.05mm)

------ VPol ------ HPol ------ RCP

Rain Rate on Oct 18, mm/hr

Rain Rate on Oct 23rd, mm/hr


Emissivity in box on 18th Emissivity in box on 23rd Emissivity in box on 27th

Frequency in GHz Frequency in GHz Frequency in GHz

Surface Emissivity – Rain Event

MiRS F18 SSMI/S (RPW > 0.05mm)

Day in October

Es

Rain Rate on Oct. 18, mm/hr

Rain Rate on Oct. 23, mm/hr


19.35V channel 37.0 V channel

October

Es

MiRS F18 SSMI/S Oct. 18, RR

19.35V channel 37.0 V channel


MiRS F18 SSMI/S Oct.18, 37V E MiRS SSMI/S Oct22, 37V E

18th 23rd 27th



MiRS SSMI/S Oct. 23, 37V E MiRS SSMI/S Oct. 27, 37V E

Channel Freq. (MHz): 1 = 50.3 H 2 = 52.8 H 3 = 53.6 H 4 = 54.4 H 5 = 55.5 H

6 = 57.29 RCP 7 = 59.4 RCP

8 = 150 H 9,10,11 = 183.31 H 12,13 = 19.35 H/V

14 = 22.235 V 15,16 = 37 H/V

17,18 = 91.655 V/H 19 = 63.28 RCP

20-24 = 60.79 RCP

Note: difference in color bar range

F18 SSMI/S Surface Emissivity Correlation Matrix In

Non & Rainy Conditions over Land

Rainy (RPW>0.05mm) Land Surf. Emissivity Correlation Matrix from 5,000 scenes Oct. 2010

Non-Precipitating Land Surface Emissivity Correlation Matrix from 53,000 scenes Oct. 2010

Rainy (RPW>0.05mm) Land Surf. Emissivity Correlation Matrix from 5,000 scenes Oct. 2010

Non-Precipitating Land Surface Emissivity Correlation Matrix from 53,000 scenes Oct. 2010

Channel Freq. (MHz): 1 = 50.3 H 2 = 52.8 H 3 = 53.6 H 4 = 54.4 H 5 = 55.5 H

6 = 57.29 RCP 7 = 59.4 RCP

8 = 150 H 9,10,11 = 183.31 H 12,13 = 19.35 H/V

14 = 22.235 V 15,16 = 37 H/V

17,18 = 91.655 V/H 19 = 63.28 RCP

20-24 = 60.79 RCP

Note: difference in color bar range

SSMI/S Surface Emissivity Correlation Matrix Clear & Rainy Conditions over Land

MiRS Testbed: Stats

Comparison to 24 hr. PE to CPC Rain Gauge Analysis 2010-06-26 (top) and time series of PoD and Correlation from May 2009 – Aug. 2010

38

Parameters are Retrieved Simultaneously: How is Emissivity constrained?

X is the solution

F(X) Fits Ym within Noise levels

X is a solution

Necessary Condition (but not sufficient)

If X is the set of parameters that impact the radiances Ym, and F the Fwd Operator

If F(X) Does not Fit Ym within Noise

X is not the solution

All parameters are retrieved simultaneously to fit all radiances together

Suggests it is not recommended to use independent algorithms for different parameters, since they don’t guarantee the fit to the radiances

 Emissivity is simply added to the state vector to be retrieved in the Bayesian Algorithm

 It is constrained by:   Background covariance (spectral constraints)   Physical constraints (CRTM)   Fitting the radiances

Atmospheric Covariances Generation

Water Vapor (ECMWF 60) Temperature (ECMWF 60) Cloud Liquid Water (WRF)

Graupel (WRF) Rain Water (WRF) . Representative sets of profiles of temp., moisture, hydrometeors are gathered from different sources (ECMWF, MM5, WRF, GDAS, etc)

. For multiple seasons and a number of latitude ranges (tropical, midlatitude, high latitude).

. These datasets are then used to generate stratified covariances to be used by MiRS.

Future improvement: Stratification of Covariances

Mid-latitude Profiles

Tropical Profiles

Rain

Rain

Ice

Ice

WRF Model Simulation

WRF Model Simulation

  Differences in vertical structures of ice, cloud, rain

  Differences in how temperature and moisture correlate to hydrometeors

  Differences in how rainfall rate relate to integrated values of rain and ice (IWP, RWP)

Atmospheric Covariance Matrix

New Atmospheric Background Covariance Matrix based on ECMWF 60, and WRF simulations over tropic oceans

performed during SON season

Cloud liquid, Rain and Ice water from WRF

MiRS Current Atmospheric Background Covariance Matrix based on Global ECMWF 60, and tropic-ocean

MM5 simulations

Temperature, Water Vapor and CLW from ECMWF 60

Rain and Ice water from MM5

Temperature and Water Vapor from ECMWF 60

Noticeable Differences noticed in covariances, especially in hydrometeors. Impact assessment on RR performances in progress

More Information  Publications

  S.A. Boukabara, F. Weng and Q. Liu, Passive Microwave Remote Sensing of Extreme Weather Events Using NOAA-18 AMSUA and MHS. IEEE Trans. on Geoscience and Remote Sensing, July 2007. Vol 45, (7), 2228-2246

  S.A. Boukabara, F. Weng, Microwave Emissivity Retrieval over Ocean in All-Weather Conditions. Validation Using Airborne GPS-Dropsondes. IEEE Trans Geos Remote Sens, 46, 376-384, 2007

  S.-A. Boukabara, K. Garrett, and W. Chen, “Global Coverage of Total Precipitable Water using a Microwave Variational Algorithm,” IEEE TGARS, vol. 48, Sept. 2010

  F. Iturbide-Sanchez, S.-A. Boukabara, R. Chen, K. Garrett, C. Grassotti, W. Chen, and F. Weng, “Assessment of a Variational Inversion System for Rainfall Rate over Land and Water Surfaces,” Submitted IEEE TGARS, July 2010.

  S.-A. Boukabara et al. “MiRS: An All-Weather 1DVAR Satellite Data Assimilation and Retrieval System,” Submitted IEEE TGARS, May 2010.

 Website http://mirs.nedsis.noaa.gov

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a physically-based rainfall rate algorithm for all

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