Global Millimeter-Wave Precipitation Retrievals Trained With A Cloud-Resolving Numerical Weather-Prediction Model, Part II: Performance Evaluation

This paper evaluates the performance of the global precipitation rate retrieval algorithm for the Advanced Microwave Sounding Unit (AMSU) that was described in Part I of this paper. AMSU is in polar orbit on several National Ocean and Atmospheric Administration (NOAA) operational weather satellites. Predicted rms retrieval errors based on a 15-km resolution 0.5-1.0-mm/h MM5 truth were 0.88, 0.83, 1.13, and 3.04 for stratiform, warm rain, ice-free rain, and convective rain, respectively, which were averaged over all view angles for land and sea up to 73deg latitude. For MM5 rates of 4-8 mm/h, these rms errors increased to 2.8, 3.4, 3.9, and 4.9 mm/h, respectively. The corresponding rms retrieval accuracies for MM5 hydrometeor water paths between 0.125 and 0.25 mm for rainwater, snow, and graupel were 0.19, 0.10, and 0.22 mm, respectively. The rms retrieval accuracy for the 0.125-0.25-m/s peak vertical wind was 0.08 m/s. Biases are small for cumulative precipitation estimates, although an upward correction factor of 1.37 is derived for convective precipitation rate probability distributions. Differences between these retrievals and those from the conically scanned Advanced Microwave Scanning Radiometer for the Earth Observing System instrument and an alternate NOAA AMSU algorithm are also characterized.     

A physical model to determine snowfall over land by microwave radiometry

Falling snow is an important component of global precipitation in extratropical regions. This paper describes the methodology and results of physically based retrievals of snow falling over land surfaces. Because microwave brightness temperatures emitted by snow-covered surfaces are highly variable, precipitating snow above such surfaces is difficult to observe using window channels that occur at low frequencies (/spl nu/<100 GHz). Furthermore, at frequencies /spl nu//spl les/37 GHz, sensitivity to liquid hydrometeors is dominant. These problems are mitigated at high frequencies (/spl nu/>100 GHz) where water vapor screens the surface emission, and sensitivity to frozen hydrometeors is significant. However, the scattering effect of snowfall in the atmosphere at those higher frequencies is also impacted by water vapor in the upper atmosphere. The theory of scattering by randomly oriented dry snow particles at high microwave frequencies appears to be better described by regarding snow as a concatenation of "equivalent" ice spheres rather than as a sphere with the effective dielectric constant of an air-ice mixture. An equivalent sphere snow scattering model was validated against high-frequency attenuation measurements. Satellite-based high-frequency observations from an Advanced Microwave Sounding Unit (AMSU-B) instrument during the March 5-6, 2001 New England blizzard were used to retrieve snowfall over land. Vertical distributions of snow, temperature, and relative humidity profiles were derived from the Mesoscale Model (MM5) cloud model. Those data were applied and modified in a radiative transfer model that derived brightness temperatures consistent with the AMSU-B observations. The retrieved snowfall distribution was validated with radar reflectivity measurements obtained from a ground-based radar network.     

Satellite Retrievals of Arctic and Equatorial Rain and Snowfall Rates Using Millimeter Wavelengths

A new global precipitation retrieval algorithm for the millimeter-wave Advanced Microwave Sounding Unit is presented that also retrieves Arctic precipitation rates over surface snow and ice. This algorithm improves upon its predecessor by excluding some surface-sensitive channels and by reducing the number of principal components (PCs) used to represent those that remain. The training sets were also modified to better represent cold regions. The algorithm still incorporates conversion of brightness temperatures to nadir, spatial filtering to better detect pixels scattering near 54 GHz, PC filtering of surface effects, and use of separate neural networks trained with the fifth-generation National Center for Atmospheric Research/Penn State Mesoscale Model (MM5) for land and sea, where warm and cold ocean are now treated differently. The validity of the snowfall detections is supported by nearly coincident CloudSat radar observations, and the physics of the model is largely validated by the reasonable agreement in annual precipitation obtained for 231 globally distributed rain gauges, including many at latitudes where snowfall dominates. Observed annual global precipitation statistics are also presented to permit comparisons with other algorithms and sensors.      

Passive microwave remote sensing of snow constrained byhydrological simulations

This paper describes a snow parameter retrieval algorithm from passive microwave remote sensing measurements. The three components of the retrieval algorithm include a dense media radiative transfer (DMRT) model, which is based on the quasicrystalline approximation (QCA) with the sticky particle assumption, a physically-based snow hydrology model (SHM) that incorporates meteorological and topographical data, and a neural network (NN) for computational efficient inversions. The DMRT model relates physical snow parameters to brightness temperatures. The SHM simulates the mass and heat balance and provides initial guesses for the neural network. The NN is used to speed up the inversion of parameters. The retrieval algorithm can provide speedy parameter retrievals for desired temporal and spatial resolutions, Four channels of brightness temperature measurements: 19V, 19H, 37V, and 37H are used. The algorithm was applied to stations in the northern hemisphere. Two sets of results are shown. For these cases, the authors use ground-truth precipitation data, and estimates of snow water equivalent (SWE) from SHM give good results. For the second set, a weather forecast model is used to provide precipitation inputs for SHM. Additional constraints in grain size and density are used. They show that inversion results compare favorably with ground truth observations     

Bayesian algorithm for microwave-based precipitation retrieval: description and application to TMI measurements over ocean

A physically oriented inversion algorithm to retrieve precipitation from satellite-based passive microwave measurements named the Bayesian algorithm for microwave-based precipitation retrieval (BAMPR) is proposed. First, we illustrate the procedure that BAMPR follows to produce precipitation estimates from observed multichannel brightness temperatures. Retrieval products are the surface rain rates, columnar equivalent water contents, and hydrometeor content profiles, together with the associated estimation uncertainties. Numerical tests performed on simulated measurements show that retrieval errors are reduced when a rain type and pattern classification procedure is employed, and that estimates are quite sensitive to the adopted error model. Finally, for different tropical storms that were observed by the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), we compare the rain retrieved from BAMPR relative to those retrieved from the Goddard Profiling (Gprof) algorithm and the Precipitation Radar-adjusted TMI estimation of rainfall (PATER) algorithm. Despite a similar inversion approach, the algorithms exhibit different performances that can be mainly related to different training databases and retrieval constraints such as cloud classification.     

Precipitation Retrieval Accuracies for Geo-Microwave Sounders

Only instruments on geostationary or comparable platforms can view global precipitation at the ~15-min interval that is necessary to monitor rapidly evolving convective events. This paper compares the abilities of 11 alternative passive microwave sensors to retrieve surface precipitation rates and hydrometeor water paths. Five instruments observe selected frequencies from 116 to 429 GHz with a filled-aperture antenna, and six instruments observe from 52 to 191 GHz with a U-shaped aperture synthesis array. The analysis is based on neural network retrieval methods and 122 global MM5-simulated storms that are generally consistent with the simultaneous Advanced Microwave Sounding Unit observations. Several instruments show considerable promise in retrieving hydrometeor water paths and 15-min average precipitation rates ~1-100 mm/h with spatial resolutions that vary from ~15 to ~50 km. This space/time resolution is potentially adequate to support assimilation of precipitation information into cloud-resolving numerical weather prediction models.     

Retrieval of snow parameters by iterative inversion of a neuralnetwork

The inversion of snow parameters from passive microwave remote sensing measurements is performed, using an iterative inversion of a neural network (NN) trained with a dense-media multiple-scattering model. Inversion of four parameters is performed based on five brightness temperatures. The four parameters are mean grain size of ice particles in snow, snow density, snow temperature, and snow depth. Iterative inversion of a data-driven forward NN model is justified on a theoretical and methodological basis. An error analysis is performed, comparing iterative inversion of a forward model with the use of an explicit inverse for the retrieval of independent snow parameters from their corresponding measurements. The NN iterative inversion algorithm is further illustrated by reconstructing a synthetic terrain of snow parameters from their corresponding measurements, inverting all four parameters simultaneously. The reconstructed parameter contours are in good agreement with the original synthetic parameter contours     

Retrieval of Atmospheric Integrated Water Vapor and Cloud Liquid Water Content Over the Ocean From Satellite Data Using the 1-D-Var Ice Cloud Microphysics Data Assimilation System (IMDAS)

Reliable prediction of precipitation by numerical weather prediction (NWP) models depends on the appropriate representation of cloud microphysical processes and accurate initial conditions of observations of atmospheric variables. Therefore, to retrieve reasonable cloud distributions, a 1-D variational Ice Cloud Microphysics Data Assimilation System (IMDAS) is developed to improve the predictability of NWP models. The general framework of IMDAS includes the Lin ice cloud microphysics scheme as a model operator, a four-stream fast microwave radiative transfer model in the atmosphere as an observation operator, and a global minimization method that is known as the shuffled complex evolution. IMDAS assimilates the satellite microwave radiometer data set of the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) and retrieves integrated water vapor and integrated cloud liquid water content. This new method successfully introduces heterogeneity into the initial state of the atmosphere, and the modeled microwave brightness temperatures agree well with the observations of the Wakasa Bay Experiment 2003 in Japan. It has significantly improved the performance of the cloud microphysics scheme by the intrusion of heterogeneity into the external global reanalysis data, which resultantly improved atmospheric initial conditions.     

AIRS/AMSU/HSB precipitation estimates

Precipitation rates (mm per hour) with 15- and 50-km horizontal resolution are among the initial products of Atmospheric Infrared Sounder/Advanced Microwave Sounding Unit/Humidity Sounder for Brazil (AIRS/AMSU/HSB). They will help identify the meteorological state of the atmosphere and any AIRS soundings potentially contaminated by precipitation. These retrieval methods can also be applied to the AMSU 23-191-GHz data from operational weather satellites such as NOAA-15, -16, and -17. The global extension and calibration of these methods are subjects for future research. The precipitation-rate estimation method presented is based on the opaque-channel approach described by Staelin and Chen (2000), but it utilizes more channels (17) and training data and infers 54-GHz band radiance perturbations at 15-km resolution. The dynamic range now reaches 100 mm/h. The method utilizes neural networks trained using the National Weather Service's Next Generation Weather Radar (NEXRAD) precipitation estimates for 38 coincident rainy orbits of NOAA-15 AMSU data obtained over the eastern United States and coastal waters during a full year. The rms discrepancies between AMSU and NEXRAD were evaluated for the following NEXRAD rain-rate categories: <0.5, 0.5-1, 1-2, 2-4, 4-8, 8-16, 16-32, and >32 mm/h. The rms discrepancies for the 3790 15-km pixels not used to train the estimator were 1.0, 2.0, 2.3, 2.7, 3.5, 6.9, 19.0, and 42.9 mm/h, respectively. The 50-km retrievals were computed by spatially filtering the 15-km retrievals. The rms discrepancies over the same categories for all 4709 50-km pixels flagged as potentially precipitating were 0.5, 0.9, 1.1, 1.8, 3.2, 6.6, 12.9, and 22.1 mm/h, respectively. Representative images of precipitation for tropical, mid-latitude, and snow conditions suggest the method's potential global applicability.     

Effects of precipitation and cloud ice on brightness temperaturesin AMSU moisture channels

Extinction by ice and rain at the AMSU frequencies used in water vapor profile retrievals is investigated with DMSP observations and brightness temperature simulations of a convective storm system. The simulations are based on mesoscale forecast model output of atmospheric, cloud, and rain profiles from which the absorption and scattering due to both liquid and frozen hydrometeors are calculated. Comparison with satellite observations indicates discrepancies of more than 90% (up to 60 K), of which only about 20% results from ignoring scattering by model-prescribed ice. The major source of error is the inability of the forecast model to produce the spatially localized high ice concentrations which cause the low microwave brightness temperatures. A criterion based on the difference between measured brightness temperatures at 183.31±3 and 183.31±1 GHz is suggested to screen out convective events before water vapor retrieval. Application to the case study examined improved agreement between simulated and observed brightness temperatures by up to a factor of two     

Retrieval of dry-snow parameters from microwave radiometric data using a dense-medium model and genetic algorithms

A numerical technique based on genetic algorithms (GAs) is used to invert the equations of an electromagnetic model based on dense-medium radiative transfer theory (DMRT) to retrieve snow depth, mean grain size, and fractional volume from microwave brightness temperatures. In order to study the sensitivity of the GA to its parameters, the technique is initially tested on simulated microwave data with and without adding a random noise. A configuration of GA parameters is selected and used for the retrieval of snow parameters from both ground-based observations and brightness temperatures recorded by the Advanced Microwave Scanning Radiometer-EOS (AMSR-E). Retrieved snow parameters are then compared with those measured on ground. Although more investigation is required, results suggest that the proposed technique is able to retrieve snow parameters with good accuracy.     

Inversion of snow parameters from passive microwave remote sensingmeasurements by a neural network trained with a multiple scatteringmodel

The inversion of snow parameters from passive microwave remote sensing measurements is performed with a neural network trained with a dense-media multiple-scattering model. The input-output pairs generated by the scattering model are used to train the neural network. Simultaneous inversion of three parameters, mean-grain size of ice particles in snow, snow density, and snow temperature from five brightness temperatures, is reported. It is shown that the neural network gives good results for simulated data. The absolute percentage errors for mean-grain size of ice particles and snow density are less than 10%, and the absolute error for snow temperature is less than 3 K. The neural network with the trained weighting coefficients of the three-parameter model is also used to invert SSMI data taken over the Antarctic region     

The GSMaP Precipitation Retrieval Algorithm for Microwave Sounders—Part I: Over-Ocean Algorithm

We develop an over-ocean rainfall retrieval algorithm for the Advanced Microwave Sounding Unit (AMSU) based on the Global Satellite Mapping of Precipitation (GSMaP) microwave radiometer algorithm. This algorithm combines an emission-based estimate from brightness temperature (Tb) at 23 GHz and a scattering-based estimate from Tb at 89 GHz, depending on a scattering index (SI) computed from Tb at both 89 and 150 GHz. Precipitation inhomogeneities are also taken into account. The GSMaP-retrieved rainfall from the AMSU (GSMaP_AMSU) is compared with the National Oceanic and Atmospheric Administration (NOAA) standard algorithm (NOAA_AMSU)-retrieved data using Tropical Rainfall Measuring Mission (TRMM) data as a reference. Rain rates retrieved by GSMaP_AMSU have better agreement with TRMM estimates over midlatitudes during winter. Better estimates over multitudes over winter are given by the use of Tb at 23 GHz in the GSMaP_AMSU algorithm. It was also shown that GSMaP_AMSU has higher rain detection than NOAA_AMSU.      

Surface Emissivity of Arctic Sea Ice at AMSU Window Frequencies

A method to retrieve the surface emissivity of sea ice at the window channels of the Advanced Microwave Sounding Unit (AMSU) radiometers in the polar region is presented. The instruments are on the new-generation satellites of the U.S. National Oceanic and Atmospheric Administration (NOAA-15, NOAA-16, and NOAA-17). The method assumes hypothetical surfaces with emissivities zero and one and simulates brightness temperatures at the top of the atmosphere using profiles of atmospheric parameters, e.g., from the European Centre for Medium-Range Weather Forecasts (ECMWF) model runs, as input for a radiative transfer model. The retrieval of surface emissivity is done by combining simulated brightness temperatures with the satellite-measured brightness temperature. The AMSU window channels differ in surface penetration depths and, thus, in the surface microphysical parameters that they depend on. Lowest layer air temperatures from ECMWF are used to infer temperatures of emitting layers at different frequencies of sea ice. A complete yearly cycle of monthly average emissivities in two selected regions (first- and multiyear ice) is giving insight into the variation of emissivities in various development stages of sea ice.      

Exploring the potential for multipatch soil-moisture retrievalsusing multiparameter optimization techniques

This paper explores the potential to retrieve surface soil moisture and optical depth simultaneously for several different patches of land cover in a single pixel from dual polarization, multiangle microwave brightness temperature observations such as will be provided by, for instance, the Soil Moisture and Ocean Salinity (SMOS) mission. MICRO-SWEAT, a coupled land-surface and microwave emission model, was used in a. year-long simulation to define the patch-specific soil moisture, optical depth, and synthetic, pixel-average microwave brightness temperatures similar to those that will be provided by SMOS. The microwave emission component of MICRO-SWEAT also forms the basis of an exploratory retrieval algorithm in which the difference between (synthetic) observations of microwave brightness temperatures and modeled, pixel-average microwave brightness temperatures for different input values of soil moisture and optical depth is minimized using the shuffled complex evolution (SCE) optimization procedure. Results are presented for two synthetic pixels, one with eight patches, where only the soil moisture is retrieved, and one with five patches, where both the soil moisture and the optical depth are retrieved     

Brightness Temperatures of Snow Melting/Refreezing Cycles: Observations and Modeling Using a Multilayer Dense Medium Theory-Based Model

The ability of electromagnetic models to accurately predict microwave emission of a snowpack is complicated by the need to account for, among other things, nonindependent scattering by closely packed snow grains, stratigraphic variations, and the occurrence of wet snow. A multilayer dense medium model can account for the first two effects. While microwave remote sensing is well known to be capable of binary wet/dry discrimination, the ability to model brightness as a function of wetness opens up the possibility of ultimately retrieving a percentage wetness value during such hydrologically significant melting conditions. In this paper, the first application of a multilayer dense medium radiative transfer theory (DMRT) model is proposed to simulate emission from both wet and dry snow during melting and refreezing cycles. Wet snow is modeled as a mixture of ice particles surrounded by a thin film of water embedded in an air background. Melting/refreezing cycles are studied by means of brightness temperatures at 6.7, 19, and 37 GHz recorded by the University of Michigan Truck-Mounted Radiometer System at the Local Scale Observation Site during the Cold Land Processes Experiment-1 in March 2003. Input parameters to the DMRT model are obtained from snow pit measurements carried out in conjunction with the microwave observations. The comparisons between simulated and measured brightness temperatures show that the electromagnetic model is able to reproduce the brightness temperatures with an average percentage error of 3% (~8 K) and a maximum relative percentage error of around 8% (~20 K)     

Backpropagation neural-network-based retrieval of atmospheric water vapor and cloud liquid water from IRS-P4 MSMR

A new multiparameter retrieval algorithm based on a backpropagation neural network (BPNN) has been developed for deriving integrated water vapor (WV) and cloud liquid water (CLW) contents over oceans from brightness temperatures (BTs) measured by the Multi-frequency Scanning Microwave Radiometer (MSMR) launched onboard Indian Remote Sensing satellite IRS-P4. The MSMR measures brightness temperatures in vertical and horizontal polarizations at 6.0-, 10.65-, 18.0-, and 21.0-GHz frequencies. The data are available at three spatial grid resolutions of 150, 75, and 50 km. In this paper, a BPNN has been trained using brightness temperatures simulated through radiative transfer model and simulated surface and atmospheric parameters. The present algorithm has been compared with the operational MSMR retrieval algorithm based on statistical regression using the same dataset. The validation of WV with in situ data (Vaisala radiosonde) is presented. Moreover, comparison of WV and CLW derived from MSMR using BPNN with the finished products from the Special Sensor Microwave/Imager and the Tropical Rainfall Measuring Mission Microwave Imager has also been carried out. The complexity of the BPNN in retrieval of geophysical products, individually and simultaneously, has also been discussed. Simultaneous retrieval of WV and CLW improves the results.     

AIRS/AMSU/HSB validation

The Atmospheric Infrared Sounder/Advanced Microwave Sounding Unit/Humidity Sounder for Brazil (AIRS/AMSU/HSB) instrument suite onboard Aqua observes infrared and microwave radiances twice daily over most of the planet. AIRS offers unprecedented radiometric accuracy and signal to noise throughout the thermal infrared. Observations from the combined suite of AIRS, AMSU, and HSB are processed into retrievals of atmospheric parameters such as temperature, water vapor, and trace gases under all but the cloudiest conditions. A more limited retrieval set based on the microwave radiances is obtained under heavy cloud cover. Before measurements and retrievals from AIRS/AMSU/HSB instruments can be fully utilized they must be compared with the best possible in situ and other ancillary "truth" observations. Validation is the process of estimating the measurement and retrieval uncertainties through comparison with a set of correlative data of known uncertainties. The ultimate goal of the validation effort is retrieved product uncertainties constrained to those of radiosondes: tropospheric rms uncertainties of 1.0 degC over a 1-km layer for temperature, and 10% over 2-km layers for water vapor. This paper describes the data sources and approaches to be used for validation of the AIRS/AMSU/HSB instrument suite, including validation of the forward models necessary for calculating observed radiances, validation of the observed radiances themselves, and validation of products retrieved from the observed radiances. Constraint of the AIRS product uncertainties to within the claimed specification of 1 K/1 km over well-instrumented regions is feasible within 12 months of launch, but global validation of all AIRS/AMSU/HSB products may require considerably more time due to the novelty and complexity of this dataset and the sparsity of some types of correlative observations.     

NOAA operational hydrological products derived from the advanced microwave sounding unit

With the launch of the NOAA-15 satellite in May 1998, a new generation of passive microwave sounders was initiated. The Advanced Microwave Sounding Unit (AMSU), with 20 channels spanning the frequency range from 23-183 GHz, offers enhanced temperature and moisture sounding capability well beyond its predecessor, the Microwave Sounding Unit (MSU). In addition, by utilizing a number of window channels on the AMSU, the National Oceanic and Atmospheric Administration (NOAA) expanded the capability of the AMSU beyond this original purpose and developed a new suite of products that are generated through the Microwave Surface and Precipitation Products System (MSPPS). This includes precipitation rate, total precipitable water, land surface emissivity, and snow cover. Details on the current status of the retrieval algorithms (as of September 2004) are presented. These products are complimentary to similar products obtained from the Defense Meteorological Satellite Program Special Sensor Microwave/Imager (SSMI) and the Earth Observing Aqua Advanced Microwave Scanning Radiometer (AMSR-E). Due to the close orbital equatorial crossing time between NOAA-16 and the Aqua satellites, comparisons between several of the MSPPS products are made with AMSR-E. Finally, several application examples are presented that demonstrate their importance to weather forecasting and analysis, and climate monitoring.     

AIRS near-real-time products and algorithms in support of operational numerical weather prediction

The assimilation of Atmospheric InfraRed Sounder, Advanced Microwave Sounding Unit-A, and Humidity Sounder for Brazil (AIRS/AMSU/HSB) data by Numerical Weather Prediction (NWP) centers is expected to result in improved forecasts. Specially tailored radiance and retrieval products derived from AIRS/AMSU/HSB data are being prepared for NWP centers. There are two types of products - thinned radiance data and full-resolution retrieval products of atmospheric and surface parameters. The radiances are thinned because of limitations in communication bandwidth and computational resources at NWP centers. There are two types of thinning: (1) spatial and spectral thinning and (2) data compression using principal component analysis (PCA). PCA is also used for quality control and for deriving the retrieval first guess used in the AIRS processing software. Results show that PCA is effective in estimating and filtering instrument noise. The PCA regression retrievals show layer mean temperature (1 km in troposphere, 3 km in stratosphere) accuracies of better than 1 K in most atmospheric regions from simulated AIRS data. Moisture errors are generally less than 15% in 2-km layers, and ozone errors are near 10% over approximately 5-km layers from simulation. The PCA and regression methodologies are described. The radiance products also include clear field-of-view (FOV) indicators. The residual cloud amount, based on simulated data, for FOVs estimated to be clear (free of clouds) is about 0.5% over ocean and 2.5% over land.