Retrieval of Surface Properties Based on Microwave Emissivity Spectra

Transcription

1 Retrieval of Surface Properties Based on Microwave Emissivity Spectra S-A. Boukabara, W. Chen, C. Kongoli, C. Grassotti and K. Garrett NOAA/NESDIS/STAR & JCSDA Camp Springs, Maryland, USA 2nd Workshop on Remote Sensing and Modeling of Surface Properties, June

2 Contents 1 Overview of Retrieval System Approach 2 Emissivity Assessment 3 Emissivity-Based Products Assessment 4 Other Applications & Summary 2

3 Retrieval / Radiative Transfer sensor Major Parameters Parameters for Retrieval: for RT: e anc Ra di ng Scattering Effect rfa ceori gin ati ng Ra dia nce li wel n Dow ng Su rfa ia Rad Up wel li Absorption ce- nce ref l Cl ou ect d -o ed rig in Ra d ati ng ian ce Ra dia nc e Sensing Temperature Frequency Absorption Moisture and scattering properties of material Geometry Cloud (non-precipitating) of material/wavelength interaction Vertical LiquidDistribution Precipitation Temperature Frozen precipitation of absorbing layers Pressure Skin temperature at which wavelength/absorber interaction occurs Amount Surface ofemissivity absorbent(s) (proxy parameter for all surface parameters) Shape, diameter, phase, mixture of scatterers. Su Scattering Effect Surface 3

4 How is Emissivity Handled in Algorithm? Emissivity is simply added to the state vector to be retrieved in the Bayesian Algorithm Allows point-to-point variation of emissivity Allows distinguishing Tskin signal from emissivity Allows distinguishing atmospheric and cloud signals from emissivity It is constrained by: Background covariance (spectral constraints) Physical constraints (CRTM) Fitting the radiances The Algorithm used is called the Microwave Integrated Retrieval System (MiRS) We will show results for N18 AMSU/MHS but MiRS is applied to N19, Metop-A, DMSP F16 SSMIS and AMSR-E 4

5 System Design & Architecture Raw Measurements Ready-To-Invert Radiances Level 1B Tbs External Data & Tools Radiance Processing Simple Algorithms 1st Guess Advanced Retrieval (1DVAR) Legend Inputs To Inversion Vertical Integration & Post-processing Monitored Used In Geoph. Monit. Radiometric Bias selection NEDT Matrx E Inversion MIRS Process Products Ready-To-Invert Radiances RTM Uncert. Matrx F NWP Ext. Data EDRs 5

6 System Design & Architecture Raw Measurements Ready-To-Invert Radiances Level 1B Tbs Radiance Processing External Data & Tools Legend Inputs To Inversion Advanced Algorithm Outputs Advanced 1st Guess Vertical Monitored Simple Vertical Integration and Post-Processing Integration & Retrieval Algorithms Used In Geoph. Monit. Advanced Algorithm (1DVAR) Measured Radiances Post-processing TPW Temp. Profile RWP Humidity Profile Vertical Radiometric Bias Yes IWP Solution Comparison: Fit Integration CLW Simulated selection Liq. AmountRadiances Prof Ready-To-Invert Initial State Vector NEDT Matrx E Within Noise Level? Ice. Amount Prof Rain Amount Prof Emissivity Spectrum Skin Temperature Core Products CRTM Reached Radiances No Inversion Process MIRS Update ProductsPost State Vector -Snow Pack Properties -Land Moisture/Wetness RTM Uncert. Matrx -Rain Rate F -Snow Fall Rate Processing -Wind Speed/Vector (Algorithms) -Cloud Top -Cloud Thickness NWP Ext. Data -Cloud phase New State Vector -Etc. EDRs 6

7 Contents 1 Overview of Retrieval System Approach 2 Emissivity Assessment 3 Emissivity-Based Products Assessment 4 Other Applications & Summary 7

8 Response of MIRS Emissivity to Coastal and River Transitions Emissivity is lower over ocean and higher over land. It is intermediate for coasts and rivers. Convergence is achieved in all cases River/Coast signature 8

9 Var. vs. Analytical Emissivity Simplified RT equation: Therefore: T = ε Ts Γ + T + T (1 ε ) Γ B T T B T Γ ε= T T s Four conditions (to be valid): 1 Γ 0 (not usable for opaque channels) 2 Ts T (Could be unstable) 3 Assumes surface specular (for RT validity) 4 Assumes clear sky (no cloud, no rain, no ice)9

10 Emissivity Qualitative Validation Emissivity 31 GHz Em31GHz MIRS Retrieval EM31GHz Analytical Computation Analytic Extraction: Use of GDAS Tskin and (T,Q) to compute transmittance and downwelling/upwelling radiances T = ε Ts Γ + T + T (1 ε ) Γ B T T Two conditions: B T Γ Ts T ε= Γ 0 T T s 10

11 Variational vs Analytical Emissivity (Ocean & Sea-ice) MiRS/N18 Ocean:Emissivity model Sea Ice: Analytical using GDAS Difference (Varia.-Analy.) High Differences when precip 23.8 GHz Std Dev Bias Sea Ice

12 Contents 1 Overview of Retrieval System Approach 2 Emissivity Assessment 3 Emissivity-Based Products Assessment 4 Other Applications & Summary 12

13 Emissivity-Based Products (MiRS post-processing) We search for the closest spectrum from a precomputed catalog to determine the surface parameters that correspond to the retrieved spectrum 1DVAR Core products Including Emissivity spectrum Look-Up Emissivity Catalogs: 1) 2) 3) Emiss=f(SIC, age) Emiss=f(SWE, size) Emiss=f(wind, angle, Ts) Emissivity-Based Products: (1) Snow Cover, (2) Snow Water Equivalent, (3) Sea Ice Concentration By-products: (1) Snow grain size, (2) Sea Ice age 13

14 Emissivity-based Surface Type Winter Spring Retreat of snow in Northern hemisphere & Extension of sea-ice in Southern hemisphere 14

15 Snow Cover Extent (SCE) Comparison with NRL and AMSR-E F16 MIRS F16 NRL HSS=0.599 POD=0.769 POD=0.755 FAR=0.031 FAR=0.065 MIRS SCE comparable to NRL and AMSRE over N. and E. Asia, Scandinavia, N. Canada AMSRE MIRS SCE underestimated w/r to AMSRE over C. Asia, C. Canada, U.S. AMSRE greater than MIRS and NRL over C. Asia 15

16 Snow Cover Extent (SCE) Comparison with IMS F16 MIRS Extensive snow cover AMSRE F16 NRL Less Extensive snow cover IMS False alarms 16

17 Sea Ice Fraction Comparison to AMSR-E MiRS AMSR (NASA-T2 Algorithm) 17

18 Sea Ice Fraction AND Type Multi-Year Ice Fraction Total Ice Fraction First-Year type confined to edges of sea ice Sea Ice Type (Multi-Year or First-Year), part of the emissivity catalog, is a byproduct of the Sea-Ice Concentration retrieval (from emissivity). First-Year Ice Fraction 18

19 Contents 1 Overview of Retrieval System Approach 2 Emissivity Assessment 3 Emissivity-Based Products Assessment 4 Other Applications & Summary 19

20 Other Applications Besides the more accurate retrieval of surface parameters from emissivity (instead of TBs), the inclusion of the emissivity in the retrieval allowed us to: Retrieve Precipitation over land more accurately Extend spatial coverage of standard parameters Retrieve more accurately lower layers of temperature and moisture (with surface-sensitive channels) 20

21 Rain Rate Improvement (Pentad Composites) MIRS Pentad composites of MiRS (left) and MSPPS (right) Rainfall Rate. MSPPS Improvement over the sea ice edges (significant reductions in false alarms). Due to accounting for emissivity in retrieval process 21

22 TPW Coverage Extension Smooth transition over coasts Very similar features to GDAS 22

23 Summary/Conclusion Emissivity has been implemented in the retrieval system to account for surface-sensitivity of channels Qualitative assessment of the emissivity behaviors demonstrates the product is of good quality Variational emissivity perfs: Bias and std devs depend on channel & surface type (less than 1% bias and 2-3% std dev wrt analy. estimat.) Emissivity-based surface products have been generated: Surface Type, SCE, SWE and SIC. Upcoming: WS+SM Accounting for emissivity in retrieval, allows to TPW extension over land, snow, ice and coast. Accounting for emissivity in retrieval allowed improvement of the rainfall rate performances as well. Emissivity could be viewed as a radiometric signal, free from atmospheric, cloud and Tskin signatures. 23