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Satellite Summer Workshop

Satellite Summer Workshop. Microwave Remote Sensing With a Focus on Passive millimeter wavelength -MW. Sid Ahmed Boukabara Principal Scientist, NESDIS Center for Satellite Applications and Research (STAR). Specific Focus on Precipitating Cases. Introduction.

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Satellite Summer Workshop

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  1. Satellite Summer Workshop Microwave Remote Sensing With a Focus on Passive millimeter wavelength -MW Sid Ahmed Boukabara Principal Scientist, NESDIS Center for Satellite Applications and Research (STAR)

  2. Specific Focus on Precipitating Cases Introduction Implementation of a MW Algorithm: Case of MiRS Summary & Conclusion General Description of Microwave Remote Sensing 4 1 3 5 2 Contents

  3. Introduction • Microwave Remote Sensing is perhaps one of the most exciting field in Environmental science, in terms of : • Large number of Applications: From Ocean to Stratosphere sensing • All-weather capability and all-surface coverage • Diversity of Types of Sensors: From radiometers, scatterometers, altimeters, radars, • (But perhaps more importantly) prospect for the future (1) we still have barely 22 channels on the best sensor we have, only on polar orbits with few hours update rate –at best-….imagine the extent of the applicability with MW sensors on Geo orbit, with thousands of channels at hyperspectral resolution (2) Upcoming explosion of affordable smallsats/cubesats MW sensors (3) Recent improvements in Microwave hardware technology (to reduce noise, synthetic aperture antenna for higher spatial resolution, increased spectral sampling, etc (4) Potential for turning noise into signal: hydrometeors density, particle size, vertical distribution, cryopshere characteristics, etc (5) On a riskier bet: 5G ….for an unprecedented microwave remote sensing of the lower boundary layer

  4. The Big Picture (Value Chain of the Observing Systems) Agriculture End Users Direct Users Processing Observing Systems Disasters Environmental Intelligence Calibration Air-Based Platform Citizens Numerical Prediction Models Transformation Phenomenon Decisions Industry Insurance Satellite Instruments Aggregation Operational Forecasting and Monitoring Government (local, state, federal) Fisheries Validation Surface Platform International Research & Understanding Quality Monitoring Energy Value chain of Observations Science Data Raw Measurements Society-useful Data Transport

  5. Scope • We will pay particular attention in this presentation to a narrower aspect of Microwave Remote Sensing: • Application to Environment Sensing. As opposed to other applications. • Passive Microwave Remote sensing. As opposed to Active RS. • Millimeter frequencies in the Microwave Spectrum. As opposed to sub-mm. • Microwave Sensors that are operational. As opposed to Research Sensors • RS aspect related to Transformation from Sensor Data (SDR) to Environmental Data (EDR). In other words: inversion/retrieval. • Atmosphere, Cryosphere, Hydrometeors, Land and Ocean. • Variational Remote sensing techniques. As opposed to Statistical, AI, etc. • We will not cover: • Microwave Instrument Hardware Design Aspects • Microwave Instrument Calibration Aspects • Microwave radiative transfer • Remote Sensing data validation (except briefly) • All methodologies for doing microwave remote sensing. • We will also… • Link Microwave remote sensing to satellite Data assimilation for NWP

  6. Specific Focus on Precipitating Cases Introduction Implementation of a MW Algorithm: Case of MiRS Summary & Conclusion General Description of Microwave Remote Sensing 4 1 3 5 2 Contents

  7. General Description of Microwave Remote Sensing sensor • MW Satellite Data is therefore able to invert/analyze: • Atmosphere (Temperature, moisture, aerosols, …) • Surface (ice, snow, land, ocean) • Hydrometeors (cloud, rain, suspended ice) Aerosol Radiance Cloud-originating Radiance Scattering Effect Upwelling Radiance * Surface-reflected Radiance * * * * * * * * * * * * * * * Absorption ice * Downwelling Radiance Scattering Effect Surface-originating Radiance Scattering Effect Surface

  8. Microwave Spectrum 22 GHz Moisture weak line Generally called Window regions 183 GHz strong Moisture Line 50-60 GHz Oxygen band 118 GHz Oxygen Line Towards sub-mm spectral Region (no man’s land at this point) Note: Window Regions are highly dependent on atmospheric conditions because of Continuum absorption but also because of absorption and extinction characteristics

  9. What are we sensitive to? (or what can we measure & what can impact the measurement?) • This question allows us to know what parameters we can reasonable expect to retrieve with microwave remote sensing • In microwave remote sensing, it is important to keep in mind that most of the time, channels are sensitive to multiple parameters: lower troposphere sounding channel sensitive to surface, moisture high alt sensing channel sensitive to cirrus cloud, etc. • This sensitivity is frequency-dependent and is geophysically-dependent. So parameters are retrieved with different degrees of certainty depending on the situation and the instrument considered • This makes having an accurate and dynamically ciputed Jacobian, a crucial piece of microwave remote sensing alg. • There are still parameters that are not being inverted in MW remote sensing but do have an important signal. So they contribute to the uncertainty of the parameters that are retrieved

  10. Assessment of Emissivity Signal for AMSU/MHS 23.8 GHz 31 GHz 50.3 GHz 52.8 GHz 89 GHz 157 GHz 191 GHz TPW 0-80 mm CLW=0/GWP=0 Strong signal No signal TPW 0-80 mm CLW=0/GWP=0.6 CLW=1/GWP=0 TPW 0-80 mm RWP 0-.6mm RWP 0-.6mm RWP 0-.6mm RWP 0-.6mm RWP 0-.6mm RWP 0-.6mm RWP 0-.6mm TPW 0-80 mm CLW=1/GWP=0.6 Color Scale 0 to 180

  11. Effect of Rain and Graupel Amount and Effective Radius for US Tropical Atmosphere Liq. Rain Froz. Rain • __________ Nominal Particle Size (500 microns) • - - - - - - - - Largest Particle Size (1500 microns) • _____ Hydrometeor Amount*0.5 • _____ Hydrometeor Amount*1.0 • _____ Hydrometeor Amount*2.0 Particle size has a significant impact on Microwave signal, sometimes as much or even more than the hydrometeor amount itself

  12. Effect of Rain and Graupel Amount and Effective Radius for US Tropical Atmosphere • __________ Nominal Particle Size (500 microns) • - - - - - - - - Largest Particle Size (1500 microns) • _____ Hydrometeor Amount*0.5 • _____ Hydrometeor Amount*1.0 • _____ Hydrometeor Amount*2.0 • Cloudy-sky minus clear-sky simulation Particle size has a significant impact on Microwave signal, sometimes as much or even more than the hydrometeor amount itself

  13. MW Channel Sensitivity -19GHz- (Case of Cold-Rain Profile) Delta TB is wrt Clear Sky Besides Rain & ice Impact on Tb, Emissivity, Effective Radius (of Rain) & Cloud Fraction have an important impact on the simulation

  14. MW Channel Sensitivity -37GHz- (Case of Cold-Rain Profile) Delta TB is wrt Clear Sky At 37GHz, the Effective Radius of Ice starts playing a role at high amounts. Emiss. Effect is reduced

  15. MW Channel Sensitivity -50GHz- (Case of Cold-Rain Profile) Delta TB is wrt Clear Sky At 50GHz, the Effective Radius of Rain and Ice starts playing equal roles. Emissiv effect is further reduced. Ice Amount starts having a depressive effect on Tb

  16. MW Channel Sensitivity -89GHz- (Case of Cold-Rain Profile) Delta TB is wrt Clear Sky At 89GHz, the emissivity has no impact at high rain/ice amounts, but still has a large impact in light precip. Ice amount has a significant impact

  17. MW Channel Sensitivity -165GHz- (Case of Cold-Rain Profile) Delta TB is wrt Clear Sky At 165GHz, the ice has a bigger role (amount and Reff). Emissiv effect is reduced to almost non-hydrometeor cases only. Fraction is also critical

  18. MW Channel Sensitivity -183GHz- (Case of Cold-Rain Profile) Delta TB is wrt Clear Sky At 183GHz, the Emissiv effect is un-noticeable. Fraction is important only for high amounts. Most important factor is Water Vapor Screening (TPW). Rain does not impact (amount or Reff). Ice amount is important.

  19. MW Channel Sensitivity 183+/- 10GHz- (Case of Cold-Rain Profile) Delta TB is wrt Clear Sky At 193GHz, the Emissiv effect is un-noticeable. Fraction is important only for high amounts. Water Vapor Screening (TPW) is no longer important. Ice amount& Reff important.

  20. Sensitivity to Trace Gases in Microwave Spectrum • Some trace gases have some signal in the microwave. • The following plots show these signals in down-looking configuration • In addition, these trace gases signature, add to the de-correlation between the microwave channels. • These signals are usually ignored in most MW algorithms

  21. Information Content in Rainy Profiles AMSU MHS Clear Sky 2 mm RWP 10 mm RWP

  22. Information Content in Icy Profiles AMSU MHS Clear Sky 2 mm IWP

  23. What is Unique/Positive About MW Remote Sensing • No saturation due to cloud, ice or rain: Much reduced sensitivity to Cloud (for some channels) to allow quasi all-weather sensing • A lot of sensitivity to hydrometeors for high frequencies, allowing a wide range of sensitivities to hydrometeors and therefore: • Direct measurement of geophysical parameters (within and below clouds). As opposed to relying on indirect links • Direct measurement of Hydrometeors (precipitation, cirrus cloud, convective clouds, etc) • Radiance is quasi-linearly related to temperature –Makes retrieval easier for atmospheric temperature profile • Major positive impact on Forecasting of Extreme Weather, global and regional NWP and Climate applications

  24. What are the Major Scientific (and non-scientific) Challenges in MW Remote Sensing? • Scientific Challenges: • Untangling Surface/Cloud/Atmosphere (in many channels) • High non-linearity of the Pb, especially for precipitating conditions • Non-gaussianity of the Error distributions for some parameters • Antenna challenges. Footprint size(s) constraints • Vertical resolution issues (wide weighting fcts) • Non-Scientific Challenges: • Crowded spectrum (RFI) • Gradual loss of frequencies: RFI/5G risks

  25. Types of Microwave Remote Sensing Sensors • Passive Microwave radiometers: For sensing multiple geophysical parameters (temperature, moisture, Skin temperature and surface parameters, Cloud and precipitation parameters, ..). Usually stratified by Imagers and Sounders • Active Microwave sensors –Scatterometers: Mainly for Ocean wind vector (speed and direction) • Active Microwave sensors – Altimeters: Mainly for ocean surface waveheights and slope, but increasingly for inland water bodies heights • Microwave Synthetic Aperture Radar (SAR): Extremely high spatial resolution. For a variety of products requiring high resolution (oil spill, ship detection, sea iceedge, etc) • Microwave precipitation radar: For measuring cloud, ice and rain amount, size and profile

  26. Challenging the Notion of Microwave Imager vs Microwave Sounder • Proper Sounders Need Imaging channels to accurately sound lower troposphere, and account for surface and cloud contamination • Imaging channels, have different sensitivities (or penetration depths) to Water Vapor continuum, just like sounding channels, so an imager has sounding capabilities • MW imaging channels have also different penetration depths for hydrometeors and present potential for allowing hydrometeor profiling • We should view MW sensors as a whole: with both imaging and sounding channels.

  27. Specific Focus on Precipitating Cases Introduction Implementation of a MW Algorithm: Case of MiRS Summary & Conclusion General Description of Microwave Remote Sensing 4 1 3 5 2 Contents

  28. Goal • In 2007, MiRS Algorithm: • Goal is to develop a physical retrieval that applies to all microwave sensors (conical, x-track, sounders, imagers), and be easy to extend to new ones. Operational for 8 satellites including JPSS • In 2014, a New generation System was put together MIIDAPS • Goal was to build on MiRS heritage and develop a system with a dual use: Retrieval and Data Assimilation Pre-processing. • Goal is to cross-fertilize DA and retrieval systems. And embed Remote Sensing Algorithms into DA systems for QC, pre-processing

  29. MiRS/MIIDAPS MIIDAPS Applies by design to sensors for which we have a forward operator (FO) Forward Operator (CRTM) X: State vector of Geophysical Parameters (T, Q, Tskin, etc) Y: State vector of Radiometric Measurements (MW, IR, etc) K: Jacobians dY/dX MiRS/ MIIDAPS Y + K X (analysis) MIIDAPS works for MW (imagers and sounders).

  30. So How does MIIDAPS Work? • Coupledapproach (emissivity part of the state vector) therefore works over all surfaces (ocean, land, cryosphere) • Uses CRTM as forward model (and therefore works whenever CRTM works) –for a large number (all) sensors. • Allows maximum information content extraction: simultaneous inversion of all parameters • Outputs used as products or as pre-processing information for satellite data assimilation

  31. Benefits of 1D+DA Approach • Takes high-non-linearity outside of the DA • Provide T, Q, TPW, in rainy/cloudy conditions for assimilation • Provide a dynamic emissivity as boundary condition • Universal tool for all satellite data for which CRTM is valid • Excellent QC tool for cloud detection, rain detection, RFI, … • Adjust background displacement for storms, hurricanes, etc. • Consistencyof parameters is ensured through DA analysis

  32. Necessary Condition (but not sufficient) F(X) Fits Ym within Noise levels If F(X) Does not Fit Ym within Noise X is not the solution X is a solution X is the solution All parameters are retrieved simultaneously to fit all radiances together (in EOF space: It is not recommended to use independent algorithms for different parameters, since they don’t guarantee the fit to the radiances Importance of Simultaneous Retrieval If X is the set of parameters that impact the radiances Ym, and F the Fwd Operator

  33. Core Retrieval Mathematical Basis Bayes Theorem (of Joint probabilities) In plain words: Main Goal in ANY Retrieval System is to find a vector X with a maximum probability of being the source responsible for the measurements vector Ym Mathematically: Main Goal in ANY Retrieval System is to find a vector X: P(X|Ym) is Max =1

  34. Probability PDF Assumed Gaussian around Background Y(X) with a Covariance E Probability PDF Assumed Gaussian around Background X0 with a Covariance B Mathematically: T T T T ì ì ü ü é é ù ù é é ù ù 1 1 1 1 1 1 m m 1 1 m m - - - - æ ö æ ö exp exp æ X X X X ö B B æ X X X X ö exp exp Y Y Y(X) Y(X) E E Y Y Y(X) Y(X) ï ï ï ï - - - - ´ ´ ´ ´ - - ´ ´ - - - - ´ ´ ´ ´ - - æ æ ö ö æ æ ö ö ê ê ú ú ê ú ê ú ç ç ÷ ÷ ç ç ÷ ÷ í í ç ç ÷ ÷ ç ç ÷ ÷ ý ý 0 0 0 0 2 2 2 2 ê ú ê ú ê ê ú ú è è ø ø è è ø ø è è ø ø è è ø ø ï ï ï ï ê ê ú ú ê ú ê ú ë ë û û ë û ë û î î þ þ Core Retrieval Mathematical Basis Maximizing In plain words: Main Goal in ANY Retrieval System is to find a vector X with a maximum probability of being the source responsible for the measurements vector Ym Is Equivalent to Minimizing Mathematically: Main Goal in ANY Retrieval System is to find a vector X: P(X|Ym) is Max Which amounts to Minimizing J(X) –also called COST FUNCTION – Same cost Function used in 1DVAR Data Assimilation System Problem reduces to how to maximize:

  35. Jacobians & Radiance Simulation from Forward Operator: CRTM Variational Mathematical Basis • Cost Function to Minimize (similar between retrieval& assimilation): • To find the optimal solution, solve for: • Assuming Linearity • This leads to iterative solution: Same Methodology applied to all parameters including hydrometeors

  36. A variable transformation is needed for Q, Hydr Rely on CRTM Usually valid Valid assumption. Assumptions made with MIRS/MIIDAPS • The PDF of X is assumed Gaussian • Operator Y able to simulate measurements-like radiances • Errors of the model and the instrumental noise combined are assumed (1) non-biased and (2) Normally distributed. • Forward model assumed locally linear at each iteration. • CRTM used in MIRS to provide: • Simulation of Radiances and • Jacobians for all parameters

  37. Comparison: Fit Within Noise Level ? Yes Simulated Radiances Measurement & RTM Uncertainty Matrix E Update State Vector Forward Operator (CRTM) New State Vector Geophysical Mean Background Geophysical Covariance Matrix B Variational Retrieval/Assimilation Measured Radiances Solution Reached No Jacobians Initial State Vector Climatology (Retrieval Mode) Forecast Field (1D-Assimilation Mode)

  38. MiRS/MIIDAPS Algorithm ApplicabilityMulti-Instrument Inversion and Data Assimilation Preprocessing System Motivation: Universal retrieval and Data Assimilation preprocessor for all satellite observations Megha-Tropiques SAPHIR S-NPP& JPSS ATMS MetOp-A AMSU/MHS MetOp-B AMSU/MHS MIIDAPS NOAA-19 AMSU/MHS NOAA-20 AMSU/MHS DMSP F16 SSMI/S DMSP F17 SSMI/S DMSP F18/19 SSMI/S TRMM TMI • Inversion Process • Inversion consistent across sensors • All parameters included in state vector • CRTM for forward/Jacobian operators • Valid over all surfaces/all-sky conditions • Use forecast, fast regression or climatology as first guess/background • DA Pre-Processing • Consistent Quality Control (rr,ice,..) • Consistent pre-processing for non-analyzed parameters • Corrects displacements (fronts,..) • Modular design, scalable • Use of MPI for HPC GPM GMI GCOM-W1 AMSR2

  39. MIRS Convergence Criteria • Convergence should check for minimal cost function J • In practice, we use non-constrained cost Function: • Convergence threshold Measurements-departure normalized by Measurements+Modeling Errors Bkg-departure normalized by Bkg Error

  40. Convergence Example • Convergence is reached everywhere: all surfaces, all weather conditions including precipitating, icy conditions Scattering Turned ON (Hydrometeors included in State Vector X) Scattering Turned OFF (non convergence when precip/ice present)

  41. Dynamic EmissivityCase of Coastal Transitions MiRS Emiss Weekly composite • Emissivity is to be considered highly variable, especially when rain is present, or when it rained a few hours/days ago or at the coastal and sea-ice transitions. • 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 MiRS TPW Weekly composite Emissivity is dynamically inverted within state vector, therefore allowing a point-to-point variation. Distinction between emissivity and atmospheric signals (including rain) is entirely physical and depends on Jacobians

  42. Emissivity Catalogs: • Emiss=f(SIC, age) • Emiss=f(SWE, size) • Emiss=f(wind, angle, Ts) Look-Up Emissivity-Based Products (Sea Ice Concentration, Snow Water Equivalent, etc) Emissivity-Based Products(MiRS post-processing) We search for the closest spectrum from a pre-computed catalog to determine the surface parameters that correspond to the retrieved spectrum 1DVAR Core products Including Emissivity spectrum

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

  44. Sea Ice Fraction AND Type Total Ice Fraction Multi-Year 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 by-product of the Sea-Ice Concentration retrieval (from emissivity). First-Year Ice Fraction

  45. TBn TB1 TB2 X1 X2 X0 Xn 1DVAR Retrieval 1DVAR Retrieval 1DVAR Retrieval B S1 S2 Sn Time Tn T1 T2 Remote Sensing Approach in Geostationary Mode (or in High Update Rate MW Observation Mode) 2DVAR: take advantage of time dimension Uncertainty matrix S: • For every point location: (2DVAR suggested approach) • Perform initial 1DVAR rerieval at time T1 • Output both solution (X1) and associated uncertainty matrix (S1) • Use X1 and S1 as background/1st Guess & covariance matrix to retrieval at time T2 • Repeat process

  46. 2DVAR Simulated Time-series 2dVAR 1dVAR 1DVAR 1dVAR 2dVAR 2dVAR Approach: Simulation • Using 5 GDAS analyses, a 24-hour time series was simulated using linear time-interpolation • CRTM used to simulate brightness temperatures • Regular 1DVAR applied on TBs (independent retrievals) • 2DVAR applied (Red)

  47. Specific Focus on Precipitating Cases Introduction Implementation of a MW Algorithm: Case of MiRS Summary & Conclusion General Description of Microwave Remote Sensing 4 1 3 5 2 Contents

  48. Emissivity Error Impact on Rain Lower channels are sensitive to emissivity even when it’ s raining dTb for dEs of 0.1, K 10% error in emissivity can mean more than 100% error in rainfall rate even in heavy rain Some channels are not sensitive to emissivity when rain is intense. Change in Tb for 10% change in emissivity as a function of Rainfall Rate

  49. Non-Linearity Issue in Cloudy/Rainy TBs(TB Variation as a Fct of hydrometeors) • Forward model assumed locally linear at each iteration. • Nothingprevents us from including hydrometeors in the state vector, along with T, Q, Emissivity, Tskin • TB variation vs. hydrometeors is non-linear but is locally linear, therefore compatible with variational inversion

  50. Non-Linearity Issue in Cloudy/Rainy TBs(TB Variation as a Fct of X-Y) Cross section at 28 N follows red curve for 190 GHz. TB variation in time and space is highly non-linear and is discontinuous due to non-continuity variations of hydrometeors in space, which is incompatible with variational assimilation

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