Global Change Observation Mission-Water Research Articles

Spatial resolution of sea surface temperature observed by AMSR2 on GCOM-W satellite

ABSTRACT This study developed a method to evaluate the effective spatial resolution of sea surface temperature (SST) data produced from observations of a microwave radiometer on satellites. The method was applied to 7-year SST data from the Advanced Microwave Scanning Radiometer 2 (AMSR2) on the Japan Aerospace Exploration Agency’s (JAXA) Global Change Observation Mission-Water (GCOM-W) satellite. JAXA has produced three SST products from AMSR2 observations: standard SST, 10-GHz SST, and multi-band SST. They differ in spatial resolution because the frequency used to retrieve the SST has different footprint sizes, although the resolution has not been evaluated so far. We utilized high-resolution infrared SST data from the geostationary meteorological satellite Himawari-8. The Himawari SST was spatially smoothed by a Gaussian function to identify the smoothing scale that most accurately reproduces the AMSR2 SST gradient fields in the same area and time. The similarities between the Himawari and AMSR2 SST gradient fields were assessed using three metrics. The correlation coefficient provided a stable and robust estimate against the noise included in the AMSR2 SSTs. As a result, the effective resolution was estimated at 67 km for the standard SST, 52 km for the 10-GHz SST, and 63 km for the multi-band SST. These are all larger than the nominal resolution expected from the AMSR2 footprint size. The 10-GHz and multi-band SSTs had significantly higher resolutions than the standard SST.

Spatial and temporal differences in surface and subsurface meltwater distribution over Greenland ice sheet using multi-frequency passive microwave observations

Increasingly larger portions of the Greenland ice sheet are undergoing seasonal melting-refreeze cycles due to global climate warming. The cycle begins with the arrival of high temperatures and increased solar radiation in the spring and summer seasons generating meltwater on the ice sheet's surface. Meltwater percolates to deeper ice layers, either refreezing within the firn, creating longer-term meltwater pockets (firn aquifers), or generating peripheral runoff. Depending on the location and climate, the refreeze duration, the depth of infiltration, and meltwater persistence are temporally and spatially complex. Our recent study showed that multi-frequency passive microwave measurements in the 1.4 GHz to 36.5 GHz range effectively distinguished seasonal meltwater between the immediate surface and deeper firn layers at an experiment site in the accumulation zone of the southwestern Greenland ice sheet. Here, we further explored the vertically and horizontally polarized multi-frequency melt response at the pan-Greenland scale. We employed 1.4 GHz brightness temperature (TB) measurements from the NASA Soil Moisture Active Passive (SMAP) satellite and 6.9, 10.7, 18.9, and 36.5 GHz TB measurements from the JAXA Global Change Observation Mission-Water Shizuku (GCOM-W) satellite. The results show that the frequency-dependent response was consistent across the ice sheet. The multi-frequency melt indications match with lasting seasonal subsurface meltwater with delayed refreezing compared to the surface. These results suggest persistent seasonal subsurface meltwater occurrences that are spatially and temporally significant but concealed from the high-frequency observations. Retrieving the meltwater evolution in snow and firn presents a complex problem; this work represents an initial step toward developing an ice-sheet-wide algorithm for more comprehensive retrieval of the meltwater profile.

Evaluation of SMOS, SMAP, AMSR2 and FY-3C soil moisture products over China

Microwave remote sensing can provide long-term near-surface soil moisture data on regional and global scales. Conducting standardized authenticity tests is critical to the effective use of observed data products in models, data assimilation, and various terminal scenarios. Global Land Data Assimilation System (GLDAS) soil moisture data were used as a reference for comparative analysis, and triple collocation analysis was used to validate data from four mainstream passive microwave remote sensing soil moisture products: Soil Moisture and Ocean Salinity (SMOS), Soil Moisture Active and Passive (SMAP), Global Change Observation Mission–Water using the Advanced Microwave Scanning Radiometer 2 (AMSR2) instrument, and Fengyun-3C (FY-3C). The effects of topography, land cover, and meteorological factors on the accuracy of soil moisture observation data were determined. The results show that SMAP had the best overall performance and AMSR2 the worst. Passive microwave detection technology can accurately capture soil moisture data in areas at high altitude with uniform terrain, particularly if the underlying surface is soil, and in areas with low average temperatures and little precipitation, such as the Qinghai–Tibet Plateau. FY-3C performed in the middle of the group and was relatively optimal in northeast China but showed poor data integrity. Variation in accuracy between products, together with other factors identified in the study, provides a baseline reference for the improvement of the retrieval algorithm, and the research results provide a quantitative basis for developing better use of passive microwave soil moisture products.

Retrieving Aerosol Optical Depth and High Spatial Resolution Ocean Surface Wind Speed From CALIPSO: A Neural Network Approach

A neural network nonlinear regression algorithm is developed for retrieving ocean surface wind speed from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) lidar measurements. The neural network is trained with CALIPSO ocean surface and atmospheric backscatter measurements together with collocated Advanced Microwave Scanning Radiometer for EOS (AMSR-E) ocean surface wind speed. Ocean surface wind speeds are derived by applying the neural network algorithm to CALIPSO measurements between 2008 and 2020. CALIPSO wind speed measurements of 2015 are also compared with Advanced Microwave Scanning Radiometer 2 (AMSR-2) measurements on the Global Change Observation Mission–Water “Shizuku” (GCOM-W) satellite. Aerosol optical depths are then derived from CALIPSO’s ocean surface backscatter signal and theoretical ocean surface reflectance calculated from CALIPSO wind speed and Cox-Munk wind–surface slope variance relation. This CALIPSO wind speed retrieval technique is an improvement from our previous studies, as it can be applied to most clear skies with optical depths up to 1.5 without making assumptions about aerosol lidar ratio.

An Attempt to Improve Snow Depth Retrieval Using Satellite Microwave Radiometry for Rough Antarctic Sea Ice

Snow depth on sea ice is a major constituent of the marine cryosphere. It is a key parameter for the derivation of sea-ice thickness from satellite altimetry. One way to retrieve the basin-scale snow depth on sea ice is by satellite microwave radiometry. There is evidence from measurements and inter-comparison studies that current retrievals likely under-estimate the snow depth over deformed, rough sea ice. We follow up on an earlier study, where satellite passive microwave data were combined with information on the sea-ice topography from the satellite laser altimeter on board the Ice, Cloud and land Elevation Satellite (ICESat) in a hybrid approach. Such topography information is spatiotemporally limited because of ICESat’s operation mode. In this paper, we aim to derive a proxy for this topography information from satellite microwave radiometry. For this purpose, we co-locate parameters describing the sea-ice deformation taken from visual ship-based observations and the surface elevation standard deviation derived from ICESat laser altimetry with the microwave brightness temperatures (TB) measured via the Advanced Microwave Scanning Radiometer aboard Earth Observation Satellite (AMSR-E) and aboard Global Change Observation Mission-Water 1 (GCOM-W1) (AMSR2). We find that the TB polarization ratio at 6.9 GHz and the TB gradient ratio between 10.7 GHz (horizontal polarization) and 6.9 GHz (vertical polarization), might be suited as such a proxy. Using this proxy, we modify the above-mentioned hybrid approach and compute the snow depths on sea ice from the AMSR-E and AMSR2 data. We compare our snow depths with those of the commonly used approach, the hybrid approach, with the ship-based observations for the years 2002 through 2015 and with the measurements made by drifting buoys for the period of 2014 through 2018. We find a convincing overall agreement with the hybrid approach and some improvement over the common approach. However, our approach is sensitive to the presence of thin ice—here, the retrieved snow depths are too large; and our approach performs sub-optimally over old ice—here, the retrieved snow depths are too small. More investigations and, in particular, more evaluations are required to optimize our approach so that the snow depths retrieved for the combined AMSR-E/AMSR2 period could serve as a data set for sea-ice thickness retrieval based on satellite altimetry.

A comprehensive 4D-Var vortex initialization using a nonhydrostatic axisymmetric TC model with convection accounted for

A four-dimensional variational (4D-Var) vortex initialization (VI) system is developed for a nonhydrostatic axisymmetric numerical model with convection accounted for (the RE87 model). Derivations of the tangent linear and adjoint models of the RE87 model and the correctness checks are presented. As an initial evaluation of the 4D-Var VI system, a cost function that measures the model fit to satellite microwave retrievals of tropical cyclone (TC), warm-core temperatures and total precipitable water (TPW) from the following four polar-orbiting satellites within a slightly longer than 1-h assimilation window is minimized using the limited-memory quasi-Newton minimization algorithm: the Suomi National Polar-orbiting Partnership, NOAA-20, Fengyun-3D and Global Change Observation Mission—Water Satellite 1. An azimuthal spectral analysis in cylindrical coordinates centred on the TC centres shows that the warm core and TPW fields within TCs are dominated by the axisymmetric component. The 4D-Var VI results assimilating the axisymmetric component of the above satellite retrievals produced a significant reduction in the cost function and the norm of the gradient as the minimization process is completed. The gradient of the cost function is accurately computed by a single integration of the RE87 adjoint model. In the cases of Hurricane Florence and Typhoon Mangkhut, improved forecast of intensifications and more realistic vertical structures of all model state variables (e.g. temperature, water vapour mixing ratio, liquid water content mixing ratio, tangential and radial wind components and vertical velocity) are obtained when compared with a parallel run initialized simply by the European Centre for Medium-Range Weather Forecasts ERA5 reanalysis.

Tropospheric Moisture Sounding Using Microwave Imaging Channels: Application to GCOM-W1/AMSR2

Traditionally, space-based microwave imagers have been used exclusively for providing total atmospheric water vapor along with hydrometeor and surface parameters, using a combination of window channels. These channels operate away from the strong water vapor absorption lines used for atmospheric moisture sounding. Nevertheless, they are sensitive to the vertical distribution of moisture in the atmosphere. This is due to the fact that the water vapor absorption continuum varies monotonically, and the wing effect of the strong absorption lines has a nonconstant effect, across the microwave spectrum. Therefore, the transmittances of window channels are different, allowing a varying level of penetration into the atmospheric column. To demonstrate the capability of imagers to operate as sounders, we introduce penetration depth, which defines the vertical sensitivity of window channels to moisture. In addition, we evaluate the degree to which data from spaceborne microwave imagers, specifically the Global Change Observation Mission-Water 1 (GCOM-W1) Advanced Microwave Scanning Radiometer-2 (AMSR2) sensor, can be used to perform sounding of tropospheric moisture. The capability is assessed using numerical weather prediction (NWP) analysis as the reference. In simulations using a regression approach, the performance of AMSR2 moisture sounding matches that of Suomi-NPP ATMS up to 700 hPa, with standard deviations of 10%–20%. AMSR2 outperforms other pure moisture sounders (Microwave Humidity Sounder and Sondeur Atmospherique du Profil d’Humidite Intertropicale par Radiometrie) up to 500 hPa, before degrading rapidly. The performances using real observations and a physical algorithm are consistent with those found in simulations. These results highlight new applications for microwave imagers like GCOM-W1 AMSR2, including in Nowcasting and NWP, which are heavily reliant on lower tropospheric moisture information.

Estimation of wind speeds inside Super Typhoon Nepartak from AMSR2 low-frequency brightness temperatures

Accurate estimations of typhoon-level winds are highly desired over the western Pacific Ocean. A wind speed retrieval algorithm is used to retrieve the wind speeds within Super Typhoon Nepartak (2016) using 6.9- and 10.7-GHz brightness temperatures from the Japanese Advanced Microwave Scanning Radiometer 2 (AMSR2) sensor on board the Global Change Observation Mission-Water 1 (GCOM-W1) satellite. The results show that the retrieved wind speeds clearly represent the intensification process of Super Typhoon Nepartak. A good agreement is found between the retrieved wind speeds and the Soil Moisture Active Passive wind speed product. The mean bias is 0.51 m/s, and the root-mean-square difference is 1.93 m/s between them. The retrieved maximum wind speeds are 59.6 m/s at 04:45 UTC on July 6 and 71.3 m/s at 16:58 UTC on July 6. The two results demonstrate good agreement with the results reported by the China Meteorological Administration and the Joint Typhoon Warning Center. In addition, Feng-Yun 2G (FY-2G) satellite infrared images, Feng-Yun 3C (FY-3C) microwave atmospheric sounder data, and AMSR2 brightness temperature images are also used to describe the development and structure of Super Typhoon Nepartak.

GCOM-W AMSR2 Soil Moisture Product Validation Using Core Validation Sites

The Advanced microwave scanning radiometer 2 (AMSR2) is part of the global change observationmission-water (GCOM-W). AMSR2 has filled the gap in passive microwave observations left by the loss of theAMSR–earth observing system (AMSR-E) after almost ten years of observations. Both missions provide brightness temperature observations that are used to retrieve soil moisture estimates at the near surface. A merged AMSR-E and AMSR2 data product will help build a consistent long-term dataset; however, before this can be done, it is necessary to conduct a thorough validation and assessment of the AMSR2 soil moisture products. This study focuses on the validation of the AMSR2 soil moisture products by comparison with in situ reference data from a set of core validation sites around the world. A total of three soil moisture products that rely on different algorithms were evaluated; the Japan Aerospace Exploration Agency (JAXA) soil moisture algorithm, the land parameter retrieval model (LPRM), and the single channel algorithm (SCA). JAXA, SCA, and LPRM soil moisture estimates capture the overall climatological features. The spatial features of the three products have similar overall spatial structure. The JAXA soil moisture product shows a lower dynamic range in the retrieved soil moisture with a satisfactory performance matrix when compared to in situ observations [unbiased root mean square error (ubRMSE) = 0.059 m3/m3, Bias = −0.083 m3 /m3, R = 0.465]. The SCA performs well over low and moderately vegetated areas (ubRMSE = 0.053 m3/m3, Bias = −0.039 m3/m3, R = 0.549). The LPRM product has a large dynamic range compared to in situ observations with a wet bias (ubRMSE = 0.094 m3 /m3, Bias = 0.091 m3/m3, R = 0.577). Some of the error is due to the difference in observation depth between the in situ sensors (5 cm) and satellite estimates (1 cm). Results indicate that overall the JAXA and SCA have the best performance based upon the metrics considered.

Application of GCOM-W AMSR2 and S-NPP ATMS Hydrological Products to a Flooding Event in the United States

Satellite remotely sensed products provide critical information to weather forecasters at the National Oceanic and Atmospheric Administration (NOAA) and greatly supplement spare or nonexistent surface observations. Low earth orbiting passive microwave sensors provide unique information related to atmospheric moisture and precipitation, as well as surface properties such as oceanic wind speed. This paper will focus on two microwave sensors that are part of NOAA's Joint Polar Satellite System—the Advanced Microwave Scanning Radiometer 2 (AMSR2) and the Advanced Technology Microwave Sounder (ATMS)—which are flown on Japan's Global Change Observation Mission—Water (GCOM-W1) and the Suomi National Polar orbiting Partnership (S-NPP) satellites. The orbits of GCOM-W1 and S-NPP are such that AMSR2 and ATMS observe the same region of the earth nearly the same time of the day. This allows for comparison of similar products from the two sensors, which is of great interest to users such as NOAA's National Weather Service. In this paper, we focus on product comparisons for a historic flooding event in the U.S. associated with Hurricane Joaquin. The microwave products are presented and shown with other widely used in-situ observations. The performance of AMSR2 and ATMS products are compared. The higher spatial resolution of the AMSR2 sensor provides more detailed information on water vapor and rain rate compared to ATMS. On the other hand, the wider swath width of the ATMS provides a more continuous field of water vapor and precipitation, as well as water vapor retrievals over land.

Validation and Comparison of LPRM Retrieved Soil Moisture Using AMSR2 Brightness Temperature at Two Spatial Resolutions in the Indian Region

The Advanced Microwave Scanning Radiometer 2 (AMSR2) on board the Global Change Observation Mission-Water 1 launched in May 2012 provides brightness temperature at two different spatial resolutions globally with an average temporal resolution of two days. Surface soil moisture is retrieved using land parameter retrieval model (LPRM) and level-3 brightness temperature data (10.65- and 36.5-GHz channels). The present LPRM implementation has AMSR2 brightness temperatures only with dielectric and vegetation parameters derived from 10.65- and 36.5-GHz channels, respectively. Retrieved soil moisture in the Indian region for 0.25° and 0.1° grid cells is validated and compared over several sites with in situ measurements. It is observed that in comparing the LPRM retrieved soil moisture with field measurements over various sites, 0.1° grid performed relatively better over 0.25° grid.

Remote Sensing of Sea Surface Temperature Using AMSR-2 Measurements

In this paper, an operational retrieval algorithm is described to infer sea surface temperature (SST) using measurements from the Advanced Microwave Scanning Radiometer-2 (AMSR-2) aboard the Global Change Observation Mission-Water. The algorithm exploits AMSR-2 observations from 12 channels (6-36 GHz, horizontally and vertically polarized), after being corrected for residual calibration biases, to retrieve SST using a statistical based scheme. The Algorithm performance is assessed and results are compared to models and other independent data products.

Urban Heat Islands as Viewed by Microwave Radiometers and Thermal Time Indices

Urban heat islands (UHIs) have been long studied using both ground-based observations of air temperature and remotely sensed thermal infrared (TIR) data. While ground-based observations lack spatial detail even in the occasional “dense” urban network, skin temperature retrievals using TIR data have lower temporal coverage due to revisit frequency, limited swath width, and cloud cover. Algorithms have recently been developed to retrieve near-surface air temperatures using microwave radiometer data, which enables characterization of UHIs in metropolitan areas, major conurbations, and global megacities at regional to continental scales using temporally denser time series than those that have been available from TIR sensors. Here we examine how UHIs appear across the entire Western Hemisphere using surface air temperatures derived from the Advanced Microwave Scanning Radiometers (AMSRs), AMSR-E onboard the National Aeronautics and Space Administration’s (NASA’s) Aqua and AMSR2 onboard the Japan Aerospace eXploration Agency’s Global Change Observation Mission-Water1 (JAXA’s GCOM-W1) satellites. We compare these data with station observations from the Global Historical Climate Network (GHCN) for 27 major cities across North America (in 83 urban-rural groupings) to demonstrate the capability of microwave data in a UHI study. Two measures of thermal time, accumulated diurnal and nocturnal degree-days, are calculated from the remotely sensed surface air temperature time series to characterize the urban-rural thermal differences over multiple growing seasons. Daytime urban thermal accumulations from the microwave data were sometimes lower than in adjacent rural areas. In contrast, station observations showed consistently higher day and night thermal accumulations in cities. UHIs are more pronounced at night, with 55% (AMSRs) and 93% (GHCN) of urban-rural groupings showing higher accumulated nocturnal degree-days in cities. While urban-rural thermal gradients may vary according to different datasets or locations, day-night differences in thermal time metrics were consistently lower (>90% of urban-rural groupings) in urban areas than in rural areas for both datasets. We propose that the normalized difference accumulated thermal time index (NDATTI) is a more robust metric for comparative UHI studies than simple temperature differences because it can be calculated from either station or remotely sensed data and it attenuates latitudinal effects.

Application of AMSR-E and AMSR2 Low-Frequency Channel Brightness Temperature Data for Hurricane Wind Retrievals

We present a method to retrieve wind speeds in hurricanes from spaceborne passive microwave radiometer data. Brightness temperature $(T_{B})$ observations acquired at the 6.9-GHz horizontal polarization channel by the AMSR-E and AMSR2 onboard the Earth Observing System Aqua and Global Change Observation Mission-Water 1 satellites are selected for wind retrieval due to the fact that the signal at this frequency is sensitive to high wind speeds but less sensitive to rain scatter than those acquired at other higher frequency channels. The AMSR-E and AMSR2 observations of 53 hurricanes between 2002 and 2014 are collected and collocated with stepped-frequency microwave radiometer (SFMR) measurements. Based on the small slope approximation/small perturbation method model and an ocean surface roughness spectrum, the wind speeds are retrieved from the $T_{B}$ data and validated against the SFMR measurements. The statistical comparison of the entire data set shows that the bias and root-mean-square error (RMSE) of the retrieved wind speeds are 1.11 and 4.34 m/s, respectively, which suggests that the proposed method can obtain high wind speeds under hurricane conditions. Two case studies show that the wind speed retrieval bias and RMSE are 1.08 and 3.93 m/s for Hurricane Earl and 0.09 and 3.23 m/s for Hurricane Edouard, respectively. The retrieved wind speeds from the AMSR-E and AMSR2 continuous three-day observations clearly show the process of hurricane intensification and weakening.

Precipitation From the Advanced Microwave Scanning Radiometer 2

The Goddard Profiling Algorithm 2010 Version 2 (GPROF2010V2) was adapted for use for the Advanced Microwave Scanning Radiometer 2 (AMSR2) on board the Global Change Observation Mission-Water satellite. This study presents the validation and evaluation of the National Oceanic and Atmospheric Administration's AMSR2 precipitation product. Rain rates calculated by GROF2010V2 meet mission accuracy requirements, with root-mean-squared error of 1.3 mm·h <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> and 3.6 mm·h <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> over ocean and land, respectively. In addition to meeting instantaneous rain rate requirements, GPROF2010V2 captures monthly and seasonal precipitation patterns relative to rain gauge analysis. Recurring problems with the algorithm over land surfaces, such as false alarms due to snow cover and surface contamination, are identified. These deficiencies can be mitigated using separate day/night screening procedures and ancillary information on daily snow cover.

Assimilating Retrievals of Sea Surface Temperature from VIIRS and AMSR2

AbstractExperiments are carried out to assess the potential contributions of two new satellite datasets, derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) on board the Suomi–National Polar-Orbiting Partnership satellite and the Advanced Microwave Scanning Radiometer 2 (AMSR2) on board the Global Change Observation Mission–Water (GCOM-W) satellite, to the quality of global sea surface temperature (SST) analyses at the Canadian Meteorological Centre (CMC). The new datasets are assimilated both separately and together. Verification of the analyses against independent data shows that the VIIRS and AMSR2 datasets yield analyses with similar global average errors, with the VIIRS analysis performing better during some seasons and the AMSR2 analysis performing better in others. Seasonal cloudiness in some regions diminishes the availability of VIIRS retrievals, resulting in better performance by the AMSR2 analysis. Both datasets were assimilated together along with data from the Advanced Very High Resolution Radiometer (AVHRR), ice data, and in situ data in an updated version of the CMC analysis produced on a 0.1° grid. Verification against independent data shows that the new analysis performed very well, with global average standard deviation consistently better than that of the international Group for High Resolution SST (GHRSST) Multiproduct Ensemble (GMPE) real-time system. This analysis is shown to outperform the currently operational CMC SST analysis, with most of the improvement being due to its assimilation of the VIIRS and AMSR2 retrievals and a further small gain being due to changes to the analysis methodology (including higher resolution).

Geophysical Model Function for the AMSR2 C-Band Wind Excess Emissivity at High Winds

Measurements of the Advanced Microwave Scanning Radiometer 2 (AMSR2) onboard the Global Change Observation Mission–Water 1 (GCOM-W1) satellite at 6.925 and 7.3 GHz and both linear polarizations over tropical cyclones (TCs) during 2012–2014 are used to derive a new geophysical function relating the brightness temperature to the sea surface wind speed (SWS) in extreme conditions. Similar sensitivity to the SWS at close C-band frequencies allowed correcting for the atmospheric contributions to the microwave radiance and estimating the brightness temperature $(T_{B})$ at the surface under TCs, combining theoretical modeling and measured $T_{B}$ analyses. Estimated oceanic $T_{B}$ 's were regressed against the wind speeds from the Best Track Archive to derive a new geophysical model function for the wind speed excess emissivity at AMSR2 C-band microwave frequencies.

Inter-calibration Results of the Advanced Microwave Scanning Radiometer-2 Over Ocean

In this paper, the oceanic radiometric calibration biases of the Advanced Microwave Scanning Radiometer-2 (AMSR2) onboard the Global Change Observation Mission-Water (GCOM-W1) are analyzed. The double difference (DD) approach is utilized to perform inter-sensor inter-calibration for AMSR2 with the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) as the reference radiometer. This technique utilizes radiative transfer model (RTM) simulations and near-simultaneous clear-sky ocean-only observed brightness temperatures from the two microwave radiometers to estimate and correct the radiometric biases of ocean scenes for AMSR2 on a channel by channel basis.

Updated Screening Procedures for GPROF2010 over Land: Utilization for AMSR-E

AbstractThe Goddard profiling algorithm 2010 (GPROF2010) was revised for the Advanced Microwave Scanning Radiometer for Earth Observing System (EOS; AMSR-E) instrument. The GPROF2010 land algorithm was developed for the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), which observes slightly different central frequencies than AMSR-E. A linear transfer function was developed to convert AMSR-E brightness temperatures to their corresponding TMI frequency for raining and nonraining instantaneous fields of view (IFOVs) using collocated brightness temperature and TRMM precipitation radar (PR) measurements. Previous versions of the algorithm separated rain from surface ice, snow, and desert using a series of empirical procedures. These occasionally failed to separate raining and nonraining scenes, leading to failed detection and false alarms of rain. The new GPROF2010, version 2 (GPROF2010V2), presented here, prefaced the heritage screening procedures by referencing annual desert and monthly snow climatologies to identify IFOVs where rain retrievals were unreliable. Over a decade of satellite- and ground-based observations from the Interactive Multisensor Snow and Ice Mapping System (IMS) and AMSR-E allowed for the creation of a medium-resolution (0.25° × 0.25°) climatology of monthly snow and ice cover. The scattering signature of rain over ice and snow is not well defined because of complex emissivity signals dependent on snow depth, age, and melting, such that using a static climatology was a more stable approach to defining surface types. GPROF2010V2 was subsequently used for the precipitation environmental data record (EDR) for the AMSR2 sensor aboard the Global Change Observation Mission–Water 1 (GCOM-W1).

A comparison between two algorithms for the retrieval of soil moisture using AMSR-E data

A comparison between two algorithms for estimating soil moisture with microwave satellite data was carried out by using the datasets collected on the four Agricultural Research Service (ARS) watershed sites in the US from 2002 to 2009. These sites collectively represent a wide range of ground conditions and precipitation regimes (from natural to agricultural surfaces and from desert to humid regions) and provide long-term in-situ data. One of the algorithms is the artificial neural network-based algorithm developed by the Institute of Applied Physics of the National Research Council (IFAC-CNR) (HydroAlgo) and the second one is the Single Channel Algorithm (SCA) developed by USDA-ARS (US Department of Agriculture-Agricultural Research Service). Both algorithms are based on the same radiative transfer equations but are implemented very differently. Both made use of datasets provided by the Japanese Aerospace Exploration Agency (JAXA), within the framework of Advanced Microwave Scanning Radiometer–Earth Observing System (AMSR-E) and Global Change Observation Mission–Water GCOM/AMSR-2 programs. Results demonstrated that both algorithms perform better than the mission specified accuracy, with Root Mean Square Error (RMSE) ≤0.06 m3/m3 and Bias <0.02 m3/m3. These results expand on previous investigations using different algorithms and sites. The novelty of the paper consists of the fact that it is the first intercomparison of the HydroAlgo algorithm with a more traditional retrieval algorithm, which offers an approach to higher spatial resolution products.