Passive Microwave Observations Research Articles

Snow Water Equivalent Retrieval Using Active and Passive Microwave Observations

AbstractThis paper implements a newly developed combined active and passive algorithm for the retrieval of snow water equivalent (SWE) by using three‐channel active and two‐channel passive observations. First, passive microwave observations at 19 and 37 GHz are used to determine the scattering albedo of snow. An a priori scattering albedo is obtained by averaging over time series observations. Second, 13.3 GHz is introduced to formulate a three‐channel (9.6, 13.3, and 17.2 GHz) radar algorithm which reduces effects of background scattering from the snow‐soil interface, and improves SWE retrieval. In the algorithm, the bicontinuous dense media radiative transfer (DMRT‐Bic) is used to compute look‐up tables (LUTs) of both radar backscatter and radiometer brightness temperatures (TBs) of the snowpack. To accelerate the retrieval, a parameterized model is derived from LUT by regression training, which links backscatter to the scattering albedo at 9.6 GHz or 13.3 GHz and to SWE. The volume scattering of snow is obtained by subtracting the background scattering from radar observations. SWE is then retrieved through a cost function that is guided by the a priori scattering albedo obtained from the passive microwave observations. The proposed algorithm, along with the active‐only version, is evaluated against the Finnish Nordic Snow Radar Experiment (NoSREx) data set measured in 2009–2013. The combined active‐passive algorithm achieves root mean square errors (RSME) less than 27 mm and correlation coefficients above 0.68 for 2009–2010, RMSE less than 21 mm and correlation above 0.85 for 2010–2011, and RMSE less than 40 mm and correlation above 0.38 for 2012–2013.

Impact of dynamic snow density on GlobSnow snow water equivalent retrieval accuracy

Abstract. Snow water equivalent (SWE) is an important variable in describing global seasonal snow cover. Traditionally, SWE has been measured manually at snow transects or using observations from weather stations. However, these measurements have a poor spatial coverage, and a good alternative to in situ measurements is to use spaceborne passive microwave observations, which can provide global coverage at daily timescales. The reliability and accuracy of SWE estimates made using spaceborne microwave radiometer data can be improved by assimilating radiometer observations with weather station snow depth observations as done in the GlobSnow SWE retrieval methodology. However, one possible source of uncertainty in the GlobSnow SWE retrieval approach is the constant snow density used in modelling emission of snow. In this paper, three versions of spatially and temporally varying snow density fields were implemented using snow transect data from Eurasia and Canada and automated snow observations from the United States. Snow density fields were used to post-process the baseline GlobSnow v.3.0 SWE product. Decadal snow density information, i.e. fields where snow density for each day of the year was taken as the mean calculated for the corresponding day over 10 years, was found to produce the best results. Overall, post-processing GlobSnow SWE retrieval with dynamic snow density information improved overestimation of small SWE values and underestimation of large SWE values, though underestimation of SWE values larger than 175 mm was still significant.

L-Band Soil Moisture Retrievals Using Microwave Based Temperature and Filtering. Towards Model-Independent Climate Data Records

The CCI Soil Moisture dataset (CCI SM) is the most extensive climate data record of satellite soil moisture to date. To maximize its function as a climate benchmark, both long-term consistency and (model-) independence are high priorities. Two unique L-band missions integrated into the CCI SM are SMOS and SMAP. However, they lack the high-frequency microwave sensors needed to determine the effective temperature and snow/frozen flagging, and therefore use input from (varying) land surface models. In this study, the impact of replacing this model input by temperature and filtering based on passive microwave observations is evaluated. This is derived from an inter-calibrated dataset (ICTB) based on six passive microwave sensors. Generally, this leads to an expected increase in revisit time, which goes up by about 0.5 days (~15% loss). Only the boreal regions have an increased coverage due to more accurate freeze/thaw detection. The boreal regions become wetter with an increased dynamic range, while the tropics are dryer with decreased dynamics. Other regions show only small differences. The skill was evaluated against ERA5-Land and in situ observations. The average correlation against ERA5-Land increased by 0.05 for SMAP ascending/descending and SMOS ascending, whereas SMOS descending decreased by 0.01. For in situ sensors, the difference is less pronounced, with only a significant change in correlation of 0.04 for SM SMOS ascending. The results indicate that the use of microwave-based input for temperature and filtering is a viable and preferred alternative to the use of land surface models in soil moisture climate data records from passive microwave sensors.

Cloudy-sky land surface temperature from VIIRS and MODIS satellite data using a surface energy balance-based method

Land surface temperature (LST) has been effectively retrieved from thermal infrared (TIR) satellite measurements under clear-sky conditions. However, TIR satellite data are often severely contaminated by clouds, which cause spatiotemporal discontinuities and low retrieval accuracy in the LST products. Several solutions have been proposed to fill the “gaps”; however, a majority of these possess constraints. For example, fusion methods with microwave data suffer from coarse spatial resolution and diverse land cover types while spatial-temporal interpolation methods neglect cloudy cooling effects. We developed a novel method to estimate cloudy-sky LST from polar-orbiting satellite data based on the surface energy balance (SEB) principle. First, the hypothetical clear-sky LST of missing or likely cloud-contaminated pixels was reconstructed by assimilating high-quality satellite retrievals into a time-evolving model built from reanalysis data using a Kalman filter data assimilation algorithm. Second, clear-sky LST was hypothetically corrected by accounting for cloud cooling based on SEB theory. The proposed method was applied to Visible Infrared Imaging Radiometer Suite (VIIRS) and Moderate Resolution Imaging Spectroradiometer (MODIS) data, and further validated using ground measurements of fourteen sites from SURFRAD, BSRN, and AmeriFlux in 2013. VIIRS LST recovered from cloud gaps exhibited a root mean square error (RMSE) of 3.54 K, a bias of −0.36 K, R2 of 0.94, and sample size (N) of 2411, comparable to the accuracy of clear-sky LST products and cloudy-sky LST estimation from MODIS (RMSE of 3.69 K, bias of −0.45 K, R2 of 0.93, and N of 2398). Thus, the proposed method performs well across different sensors, seasons, and land cover types. The abnormal retrieval values caused by cloud contamination were also corrected in the proposed method. The overall accuracy was better than the downscaled cloudy-sky LST retrieved from passive microwave (PMW) observations and former SEB-based cloudy-sky LST estimation methods. Validation using time-series measurements showed that the all-sky LST time series, including both clear- and cloudy-sky retrievals, can capture realistic variability without sudden abruptions or discontinuities. RMSE values for the all-sky LST varied from 2.54 to 4.15 K at the fourteen sites. Spatially continuous LST maps over the Contiguous United States were compared with corresponding maps from PMW data in the winter and summer of 2018, exhibiting similar spatial patterns but with additional spatial details. Moreover, sensitivity analysis suggested that the reconstruction of clear-sky LST dominantly impacts the accuracy of cloudy-sky LST estimation. The proposed method can be potentially implemented in similar satellite sensors for global real-time production.

Forest Canopy Changes in the Southern Amazon during the 2019 Fire Season Based on Passive Microwave and Optical Satellite Observations

Canopy dynamics associated with fires in tropical forests play a critical role in the terrestrial carbon cycle and climate feedbacks. The aim of this study was to characterize forest canopy dynamics in the southern Amazon during the 2019 fire season (July–October) using passive microwave-based vegetation optical depth (VOD) and three optical-based indices. First, we found that precipitation during July–October 2019 was close to the climatic means, suggesting that there were no extreme hydrometeorological events in 2019 and that fire was the dominant factor causing forest canopy anomalies. Second, based on the active fire product (MCD14ML), the total number of active fires over each grid cell was calculated for each month. The number of active fires during the fire season in 2019 was above average, particularly in August and September. Third, we compared the anomalies of VOD and optical-based indices (the normalized difference vegetation index (NDVI), the enhanced vegetation index (EVI), and the normalized burn ratio (NBR)) against the spatiotemporal distribution of fires during July–October 2019. Spatially, the location with a concentrated distribution of significant negative VOD anomalies was matched with the grid cells with fire activities, whereas the concentrated distribution of strong negative anomalies in optical-based indices were found in both burned and unburned grid cells. When we focused on the temporal pattern over the grid cells with fire activity, the VOD and the optical-based indices behaved similarly from July to October 2019, i.e., the magnitude of negative anomalies became stronger with increased fire occurrences and reached the peak of negative anomalies in September before decreasing in October. A discrepancy was observed in the magnitude of negative anomalies of the optical-based indices and the VOD; the magnitude of optical-based indices was larger than the VOD in August–September and recovered much faster than the VOD over the grid cells with relatively low fire activity in October. The most likely reason for their different responses is that the VOD represents the dynamics of both photosynthetic (leaf) and nonphotosynthetic (branches) biomass, whereas optical-based indices are only sensitive to photosynthetic (leaf) active biomass, which recovers faster. Our results demonstrate that VOD can detect the spatiotemporal of canopy dynamics caused by fire and postfire canopy biomass recovery over high-biomass rainforest, which enables more comprehensive assessments, together with classic optical remote sensing approaches.

Differences in the Diurnal Variation of Precipitation Estimated by Spaceborne Radar, Passive Microwave Radiometer, and IMERG

AbstractAccurate, high‐resolution measurements of the precipitation diurnal cycle are important for understanding local variations in precipitation and the underlying processes which cause them. Combining 16 years of measurements from the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) and 4 years from the Global Precipitation Measurement (GPM) Dual‐frequency Precipitation Radar (DPR) creates a 20‐year mean of near surface precipitation rate at 0.1° × 0.1°, hourly resolution from 35°N to 35°S. Similar data sets are created using the same proportions of the TRMM Microwave Imager and GPM Microwave Imager measurements, and 18 years of the Integrated Multi‐satellitE Retrievals for GPM (IMERG) precipitation product. The phase and amplitude of the diurnal variation from each data set are calculated at each grid point using a fast Fourier transform (FFT). The satellite data are validated with 14 years of hourly National Climatic Data Center rain gauge measurements over the southeast United States by applying the same FFT method. Spaceborne radar best represents the unaveraged magnitude and diurnal phase measured by the gauges in this region. Time delays are found in precipitation retrievals from passive microwave observations as well as in the IMERG product. The strengths and weaknesses of these high‐resolution data sets in determining the diurnal cycle of precipitation on a climatological scale are discussed across the tropics and subtropics. In general, when compared to PR and DPR retrievals, IMERG overestimates the global precipitation and has varying time lags, which tend to be larger and earlier over regions where organized convective systems are common.

A comprehensive five-year evaluation of IMERG Late Run precipitation estimates over the Netherlands

AbstractApplications like drought monitoring and forecasting can profit from the global and near real-time availability of satellite-based precipitation estimates once their related uncertainties and challenges are identified and treated. To this end, this study evaluates the IMERG V06B Late Run precipitation product from the Global Precipitation Measurement mission (GPM), a multi-satellite product that combines space-based radar, passive microwave (PMW), and infrared (IR) data into gridded precipitation estimates. The evaluation is performed on the spatiotemporal resolution of IMERG (0.1° × 0.1°, 30 min) over the Netherlands over a five-year period. A gauge-adjusted radar precipitation product from the Royal Netherlands Meteorological Institute (KNMI) is used as reference, against which IMERG shows a large positive bias. To find the origin of this systematic overestimation, the data is divided into seasons, rainfall intensity ranges, echo top height (ETH) ranges, and categories based on the relative contributions of IR, morphing, and PMW data to the IMERG estimates. Furthermore, the specific radiometer is identified for each PMW-based estimate. IMERG’s detection performance improves with higher ETH and rainfall intensity, but the associated error and relative bias increase as well. Severe overestimation occurs during low-intensity rainfall events and is especially linked to PMW observations. All individual PMW instruments show the same pattern: overestimation of low-intensity events and underestimation of high-intensity events. IMERG misses a large fraction of shallow rainfall events, which is amplified when IR data is included. Space-based retrieval of shallow and low-intensity precipitation events should improve before IMERG can be implemented over the middle and high-latitudes.

A practical reanalysis data and thermal infrared remote sensing data merging (RTM) method for reconstruction of a 1-km all-weather land surface temperature

An all-weather land surface temperature (LST) dataset at moderate to high spatial resolutions (e.g. 1 km) has been in urgent need, especially in areas frequently covered in clouds (i.e. the Tibetan Plateau). Merging satellite thermal infrared (TIR) and passive microwave (PMW) observations is a widely-adopted approach to derive such LST datasets, whereas the swath gap of the PMW data leads to considerable data deficiency or low reliability in the merged LST, especially at the low-mid latitudes. Fortunately, reanalyzed data provides the spatiotemporally continuous LST and thus, is promising to be merged with TIR data for reconstructing the all-weather LST without this issue. However, few studies along this direction have been reported. In this context, based on the decomposition model of LST time series, this study proposes a novel reanalysis and thermal infrared remote sensing data merging (RTM) method to reconstruct the 1-km all-weather LST. The method was applied to merge Aqua/Terra Moderate Resolution Imaging Spectroradiometer (MODIS) and Global/China Land Data Assimilation System (GLDAS/CLDAS) data over the Tibetan Plateau and the surrounding area. Results show that the RTM LST has RMSEs of 2.03–3.98 K and coefficients of determination of 0.82–0.93 under all-weather conditions when validated against the ground measured LST. Besides, from comparison between RTM LST and current existing PMW-TIR merged LST, it is found the former LST efficiently outperforms the latter one in terms of accuracy and image quality, especially over the MW swath gap-covered area. In addition, compared to the MODIS-CLDAS merged all-weather LST based on the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM), the two LSTs have comparable accuracy while the RTM LST has higher spatial completeness. The method is promising for generating a long-term all-weather LST record at moderate to high spatiotemporal resolutions at large scales, which would be beneficial to associated studies and applications.

Near-Real-Time Estimation of 1-km All-Weather Land Surface Temperature by Integrating Satellite Passive Microwave and Thermal Infrared Observations

A widely used approach for all-weather land surface temperature (LST) estimation is the integration of satellite passive microwave (MW) and thermal infrared (TIR) remote sensing observations. However, there are still few methods for estimating near-real time (NRT) all-weather (AW) (NRT-AW) LST. Besides, estimation of the LST within the swath gap of the satellite MW images is still greatly limited. This letter proposes a so-called NRT-AW method for the estimation of NRT-AW LST. NRT-AW firstly fills up the brightness temperatures (BT) inside the AMSR2 swath gap. Then, the NRT AW LST is estimated by learning the mapping between the time series of AMSR2 BT and MODIS LST on the annual scales. The results of the application of NRT-AW in the Heihe River Basin (HRB) show that the NRT-AW LST is spatially continuous and highly consistent with the original MODIS LST with a standard deviation (STD) of 1.27-1.77 K. Validation based on in situ LST indicates that the NRT-AW LST estimate has a root mean square error (RMSE) of 2.46-4.62 K. This method is beneficial for rapid mapping of all-weather LST over large areas and, thus, can satisfy associated applications.

How do different sensors impact IMERG precipitation estimates during hurricane days?

Ground observation absence in many parts of the world highlights the importance of merged satellite precipitation products. In this study, we aim to evaluate the effect of different sources of data in the uncertainties of a merged satellite product, by comparing the Integrated Multi-satellitE Retrievals for GPM (IMERG) Final product (V06B) with a ground-based radar product, Multi-Radar Multi-Sensor (MRMS), using both pixel-based and object-based approaches. This study is focused on the eastern United States (land-only) during the hurricane days that occurred in 2016–2018. The results showed that IMERG had better agreement in terms of the average precipitation intensity and area with a bias reduction of 75% and 65%, respectively, when the passive microwave (PMW) sensor overpass is matched instantaneously with MRMS compared to temporally averaged MRMS data (MRMS-Averaged). PMW observations tend to show storms with smaller areas in the IMERG Final product in comparison with MRMS, possibly due to the effect of light precipitation not detected properly by PMW sensors. However, by removing the light precipitation (less than 1 mm/h) in the object-based approach, hurricane objects in the IMERG Final product tend to be larger during the PMW observations, which might be related to different viewing angles of sensors contributing to MRMS and IMERG products. Precipitation estimates have smaller areas with higher average intensity during the PMW observations in the IMERG Final product compared to data estimated by Morphed or IR (morph/IR) observations, which is probably related to the effect of morphing technique, leading to homogenization of the varying rainstorm characteristics. In addition, with the longer absence of PMW observations, the quality of morph/IR estimates in IMERG Final product deteriorates with a decreasing correlation coefficient, a growth in precipitation area and a downward trend in average precipitation intensity. Finally, the inter-comparison of PMW sensors showed the priority of imagers over sounders with GMI as the best among imagers and MHS as the best among sounders in terms of correlation and average intensity compared to MRMS; however, SSMIS was the best in capturing the precipitation area.

Improving cold-region streamflow estimation by winter precipitation adjustment using passive microwave snow remote sensing datasets

Winter precipitation estimations and spatially sparse snow observations are key challenges when predicting snowmelt-driven floods. An improvement in streamflow prediction is achieved in a snowmelt-dominant basin, i.e. the Red River Basin (RRB), by adjusting the amounts of snowfall through satellite-borne passive microwave observations of snow water equivalent (SWE). A snowfall forcing dataset is scaled to minimize the difference between simulated and observed SWE over the RRB. Advanced microwave scanning radiometer-E (AMSR-E) SWE products serve as the observed SWE to obtain the solution to the linear equation between the AMSR-E and the baseline (no snowfall-forcing adjustment) SWE to yield a multiplication factor (M factor). In the headwaters of the RRB in the United States, a Nash–Sutcliffe efficiency (NSE) of 0.74 is obtained against observed streamflow, with M factor-adjusted streamflow during the snowmelt seasons (January to April). The baseline streamflow simulation without M factor exhibits an NSE of 0.38 owing to an underestimated SWE.

Extending the Spatio-Temporal Applicability of DISPATCH Soil Moisture Downscaling Algorithm: A Study Case Using SMAP, MODIS and Sentinel-3 Data

DISPATCH is a disaggregation algorithm of the low-resolution soil moisture (SM) estimates derived from passive microwave observations. It provides disaggregated SM data at typically 1 km resolution by using the soil evaporative efficiency (SEE) estimated from optical/thermal data collected around solar noon. DISPATCH is based on the relationship between the evapo-transpiration rate and the surface SM under non-energy-limited conditions and hence is well adapted for semi-arid regions with generally low cloud cover and sparse vegetation. The objective of this paper is to extend the spatio-temporal coverage of DISPATCH data by 1) including more densely vegetated areas and 2) assessing the usefulness of thermal data collected earlier in the morning. Especially, we evaluate the performance of the Temperature Vegetation Dryness Index (TVDI) instead of SEE in the DISPATCH algorithm over vegetated areas (called vegetation-extended DISPATCH) and we quantify the increase in coverage using Sentinel-3 (overpass at around 09:30 am) instead of MODIS (overpass at around 10:30 am and 1:30 pm for Terra and Aqua, respectively) data. In this study, DISPATCH is applied to 36 km resolution Soil Moisture Active and Passive SM data over three 50 km by 50 km areas in Spain and France to assess the effectiveness of the approach over temperate and semi-arid regions. The use of TVDI within DISPATCH increases the coverage of disaggregated images by 9 and 14% over the temperate and semi-arid sites, respectively. Moreover, including the vegetated pixels in the validation areas increases the overall correlation between satellite and in situ SM from 0.36 to 0.43 and from 0.41 to 0.79 for the temperate and semi-arid regions, respectively. The use of Sentinel-3 can increase the spatio-temporal coverage by up to 44% over the considered MODIS tile, while the overlapping disaggregated data sets derived from Sentinel-3 and MODIS land surface temperature data are strongly correlated (around 0.7). Additionally, the correlation between satellite and in situ SM is significantly better for DISPATCH (0.39–0.80) than for the Copernicus Sentinel-1-based (−0.03 to 0.69) and SMAP/S1 (0.37–0.74) product over the three studies (temperate and semi-arid) areas, with an increase in yearly valid retrievals for the vegetation-extended DISPATCH algorithm.

Precipitation Retrievals from Passive Microwave Cross-Track Sensors: The Precipitation Retrieval and Profiling Scheme

The retrieval of precipitation (snowfall and rainfall) from satellite sensors on a global basis is essential in aiding our knowledge and understanding of the Earth System and for many societal applications. Measurements from surface-based instruments are essentially limited to populated regions, necessitating the use of satellite-based observations to provide estimates of precipitation across the whole of the Earth’s surface. The temporal and spatial variability of precipitation requires adequate sampling, especially at finer resolutions. It is, therefore, necessary to exploit all available data from precipitation-capable satellites to ensure the proper representation of precipitation. To date, the estimation of precipitation using passive microwave observations has been largely concentrated upon the conically scanning imaging instruments, with relatively few techniques exploiting the observations made from the cross-track sounders. This paper describes the development of the Precipitation Retrieval and Profiling Scheme (PRPS) to retrieve precipitation from cross-track sensors, together with its performance against surface radar data and other satellite precipitation retrievals.

An evaluation of the synergy of satellite passive microwave observations between 1.4 and 36 GHz, for vegetation characterization over the Tropics

Satellite passive microwave observations from 1.4 to 36 GHz already showed sensitivity to vegetation parameters, primarily through the calculations of the Vegetation Optical Depth (VOD) at individual window frequencies, separately. Here we evaluate the synergy of this frequency range for vegetation characterization, through the estimation of two vegetation parameters, its foliage and the photosynthesis activity as described by the Normalized Difference Vegetation Index (NDVI), and its woody components and carbon stock as described by the Above Ground Carbon (AGC), using different combinations of channels in the considered frequency range. Neural network retrievals are trained on these two vegetation parameters (NDVI and AGC), for several microwave channel combinations, including the future Copernicus Imaging Microwave Radiometer (CIMR) that will observe simultaneously in window channels from 1.4 to 36 GHz, for the first time. This methodology avoids the use of any assumptions in the complex interaction between the surface (vegetation and soil) and the radiation, as well as any ancillary observations, to propose a genuine and objective evaluation of the information content of the passive microwave frequencies for vegetation characterization. Our analysis quantifies the synergy of the microwave frequencies from 1.4 to 36 GHz, and shows the expected potential of the CIMR instrument for the monitoring of vegetation parameters in tropical environments, as compared to current instruments. It also confirms that the 1.4 GHz observations have a high sensitivity to AGC, as compared to the other single frequencies up to 36 GHz, at least under tropical environments.

Growing Operational Use of FY-3 Data in the ECMWF System

This paper reviews the data quality and impact of observations from the FY-3 satellite series used operationally in the ECMWF system. This includes data from the passive microwave radiometers MWHS-1, MWHS-2 and MWRI, as well as observations from the radio occultation receiver GNOS. Evaluations against background equivalents show that the quality of the observations is broadly comparable to that of similar instruments on other polar-orbiting satellites, even though biases for the passive microwave observations can be somewhat larger and more complex for some channels. An observing system experiment shows that the FY-3 instruments jointly contribute significantly to the forecast skill in the ECMWF system. Positive impact of up to 2% is seen for most variables out to the day-2 forecasts over hemispheric scales, with significant benefits for total column water vapor or for temperature and wind in the stratosphere out to day 4.

Evaluation of Precipitation Vertical Profiles Estimated by GPM-Era Satellite-Based Passive Microwave Retrievals.

Precipitation estimation based on passive microwave (MW) observations from low-Earth-orbiting satellites is one of the essential variables for understanding the global climate. However, almost all validation studies for such precipitation estimation have focused only on the surface precipitation rate. This study investigates the vertical precipitation profiles estimated by two passive MW-based retrieval algorithms, i.e., the emissivity principal components (EPC) algorithm and the Goddard profiling algorithm (GPROF). The passive MW-based condensed water content profiles estimated from the Global Precipitation Measurement Microwave Imager (GMI) are validated using the GMI + Dual-Frequency Precipitation Radar combined algorithm as the reference product. It is shown that the EPC generally underestimates the magnitude of the condensed water content profiles, described by the mean condensed water content, by about 20%-50% in the middle-to-high latitudes, while GPROF overestimates it by about 20%-50% in the middle-to-high latitudes and more than 50% in the tropics. Part of the EPC magnitude biases is associated with the representation of the precipitation type (i.e., convective and stratiform) in the retrieval algorithm. This suggests that a separate technique for precipitation type identification would aid in mitigating these biases. In contrast to the magnitude of the profile, the profile shapes are relatively well represented by these two passive MW-based retrievals. The joint analysis between the estimation performances of the vertical profiles and surface precipitation rate shows that the physically reasonable connections between the surface precipitation rate and the associated vertical profiles are achieved to some extent by the passive MW-based algorithms.

Understanding Uncertainty of Snow Radiative Transfer Modeling Within a Mixed Deciduous and Evergreen Forest

Satellite-based passive microwave observations provide the best available continuous observational estimates of global snow water storage due to their broad geographic footprint and low sensitivity to clouds and precipitation. However, these observations are subject to substantial uncertainty due to the complex radiative properties of snow and from interference in forested areas. Physical radiative transfer models can be leveraged to improve the fidelity of these observations and as a data-assimilation tool. In this article, the Dense Media Radiative Transfer model with Multiple Layers (DMRT-ML) is used to simulate snow brightness temperatures from data collected from snow pits excavated during a two-day-long field study performed a temperate forest in the Northeast United States. The simulations are evaluated against surface-based radiometer observations collected at the snow pits. The DMRT-ML is configured with varying complexity to determine the snowpack characteristics most essential toward simulating brightness temperature within a temperate forest with complicated snow stratigraphy. In general, the single-layer configurations were not sufficiently complex to accurately simulate snow brightness temperature without significant tuning. The most accurate simulation was a two-layer configuration with a prescribed ice layer separating the snow layers. This simulation had a root-mean-square error $<; $15 K for the 37-GHz frequency. More complicated snowpack stratigraphy configurations did not substantively improve the results over the two-layer model configuration. The DMRT-ML was also used to examine differences between redundant datasets of density and grain size. It was determined that similar snow data collection and radiative transfer model configuration techniques are critical to ensure cross-study comparability.

SHARPEN: A Scheme to Restore the Distribution of Averaged Precipitation Fields

AbstractA key strategy in obtaining complete global coverage of high-resolution precipitation is to combine observations from multiple fields, such as the intermittent passive microwave observations, precipitation propagated in time using motion vectors, and geosynchronous infrared observations. These separate precipitation fields can be combined through weighted averaging, which produces estimates that are generally superior to the individual parent fields. However, the process of averaging changes the distribution of the precipitation values, leading to an increase in precipitating area and decrease in the values of high precipitation rates, a phenomenon observed in IMERG. To mitigate this issue, we introduce a new scheme called SHARPEN, which recovers the distribution of the averaged precipitation field based on the idea of quantile mapping applied to the local environment. When implemented in IMERG, precipitation estimates from SHARPEN exhibit a distribution that resembles that of the original instantaneous observations, with matching precipitating area and peak precipitation rates. Case studies demonstrate its improved ability in bridging between the parent precipitation fields. Evaluation against ground observations reveals a distinct improvement in precipitation detection skill, but also a slightly reduced correlation likely because of a sharper precipitation field. The increased computational demand of SHARPEN can be mitigated by striding over multiple grid boxes, which has only marginal impacts on the accuracy of the estimates. SHARPEN can be applied to any precipitation algorithm that produces an average from multiple input precipitation fields and is being considered for implementation in IMERG V07.

Impact of channel selection on SST retrievals from passive microwave observations

Two retrieval algorithms developed as a part of the European Space Agency Climate Change Initiative (ESA-CCI) project are used to assess the effects of withholding observations from selected frequency channels on the retrieved subskin Sea Surface Temperature (SST) from AQUA's Advanced Microwave Scanning Radiometer—Earth Observing System (AMSR-E) and to evaluate a Copernicus Imaging Microwave Radiometer (CIMR) like channel configuration.The first algorithm is a statistical regression-based retrieval algorithm, while the second is a physically based optimal estimation (OE) algorithm. A database with matching satellite and drifting buoy observations is used to test the performance of each channel configuration using both retrieval algorithms to identify the most optimal channel selection for accurate SST retrievals. The evaluation against in situ observations allows identification of the strengths and weaknesses of the two retrieval algorithms, and demonstrates the importance of using in situ observations to evaluate existing theoretical retrieval uncertainty studies. Overall, the performance increases as expected when more channels are included in the retrieval. In particular, more channels allow a better performance for the range of different observing conditions (e.g. cold waters). The two retrieval algorithms agree that for a three-channel configuration, the 6, 10, 18 GHz (V and H polarization) is better than the 6, 10, 23 GHz configuration (V and H polarization). This is demonstrated for different geographical regions and throughout all seasons. Of the different combinations tested here, it is evident that withholding observations from the 23 and 36 GHz channels from the retrieval has the least impact on the SST performance. Overall, this analysis shows that the CIMR like channel configuration performs very well when compared to an AMSR-E like constellation using both retrieval algorithms.

Microphysical properties of three types of snow clouds: implication for satellite snowfall retrievals

Abstract. Ground-based radar and radiometer data observed during the 2017–2018 winter season over the Pyeongchang area on the east coast of the Korean Peninsula were used to simultaneously estimate both the cloud liquid water path and snowfall rate for three types of snow clouds: near-surface, shallow, and deep. Surveying all the observed data, it is found that near-surface clouds are the most frequently observed cloud type with an area fraction of over 60 %, while deep clouds contribute the most in snowfall volume with about 50 % of the total. The probability distributions of snowfall rates are clearly different among the three types of clouds, with the vast majority hardly reaching 0.3 mm h−1 (liquid water equivalent snowfall rate) for near-surface, 0.5 mm h−1 for shallow, and 1 mm h−1 for deep clouds. However, the liquid water paths in the three types of clouds all have the substantial probability to reach 500 g m−2. There is no clear correlation found between snowfall rate and the liquid water path for any of the cloud types. Based on all observed snow profiles, brightness temperatures at Global Precipitation Measurement Microwave Imager (GPM/GMI) channels are simulated, and the ability of a Bayesian algorithm to retrieve snowfall rate is examined using half the profiles as observations and the other half as an a priori database. Under an idealized scenario, i.e., without considering the uncertainties caused by surface emissivity, ice particle size distribution, and particle shape, the study found that the correlation as expressed by R2 between the “retrieved” and “observed” snowfall rates is about 0.32, 0.41, and 0.62, respectively, for near-surface, shallow, and deep snow clouds over land surfaces; these numbers basically indicate the upper limits capped by cloud natural variability, to which the retrieval skill of a Bayesian retrieval algorithm can reach. A hypothetical retrieval for the same clouds but over ocean is also studied, and a major improvement in skills is found for near-surface clouds with R2 increasing from 0.32 to 0.52, while a smaller improvement is found for shallow and deep clouds. This study provides a general picture of the microphysical characteristics of the different types of snow clouds and points out the associated challenges in retrieving their snowfall rate from passive microwave observations.