Microwave Satellite Sensors Research Articles

Synthetic Aperture Radar (SAR) Compact Polarimetry for Soil Moisture Retrieval

Soil moisture is a factor for risk analysis in the agricultural sector, yet access to temporally and spatially detailed data is challenging for much of the world’s agricultural extend. Significant effort has been focused on developing methodologies to estimate soil moisture from microwave satellite sensors. Canada’s RADARSAT Constellation Mission (RCM) is capable of acquiring imagery in a number of modes with a Compact Polarimetry (CP) configuration at different spatial resolutions (1 to 100 m). RCM offers greater polarization diversity, wide swaths and improved temporal frequency (4-day exact revisit time); all important considerations for large area monitoring of agricultural resources. The major goal of this study was to examine whether CP could accurately estimate surface soil moisture over bare fields. A methodology was developed using the calibrated Integral Equation Model (IEM) multi-polarization inversion approach. RADARSAT-2 data was acquired between 2012 and 2017 over a test site in eastern Canada. CP backscatter for two RCM modes (medium resolution 30 m and 50 m (MR30 and MR50)) was simulated using 63 RADARSAT-2 fully polarimetric images. A simple transfer function was developed between RH (right circular-horizontal) and HH (horizontal-horizontal) intensity, as well as RV (right circular-vertical) and VV (vertical-vertical). These HH- and VV-like intensities were then used in the multi-polarization inversion scheme to retrieve soil moisture. CP soil moisture retrievals were validated against soil moisture measurements from a long term in-situ network instrumented with five soil moisture stations. Retrieved and measured soil moisture were well correlated (R > 0.70) with an unbiased root mean square error (ubRMSE) less than 0.06 m3/m3. Overall, the developed method clearly captured the dry down and wetting trends observed through the five years study period. However, results demonstrated that the inversion method introduced a consistent bias (~0.10 m3/m3). Comparison of CP soil moisture estimates to those from the Soil Moisture Active Passive (SMAP) passive microwave satellite confirmed this bias. This study demonstrates the potential of C-band CP data to deliver accurate soil moisture products over wide swaths for regional and national soil moisture monitoring.

Investigating the liquid water path over the tropical Atlantic with synergistic airborne measurements

Abstract. Liquid water path (LWP) is an important quantity to characterize clouds. Passive microwave satellite sensors provide the most direct estimate on a global scale but suffer from high uncertainties due to large footprints and the superposition of cloud and precipitation signals. Here, we use high spatial resolution airborne microwave radiometer (MWR) measurements together with cloud radar and lidar observations to better understand the LWP of warm clouds over the tropical North Atlantic. The nadir measurements were taken by the German High Altitude and LOng range research aircraft (HALO) in December 2013 (dry season) and August 2016 (wet season) during two Next-generation Advanced Remote sensing for VALidation (NARVAL) campaigns. Microwave retrievals of integrated water vapor (IWV), LWP, and rainwater path (RWP) are developed using artificial neural network techniques. A retrieval database is created using unique cloud-resolving simulations with 1.25 km grid spacing. The IWV and LWP retrievals share the same eight MWR frequency channels in the range from 22 to 31 GHz and at 90 GHz as their sole input. The RWP retrieval combines active and passive microwave observations and is able to detect drizzle and light precipitation. The comparison of retrieved IWV with coincident dropsondes and water vapor lidar measurements shows root-mean-square deviations below 1.4 kg m−2 over the range from 20 to 60 kg m−2. This comparison raises the confidence in LWP retrievals which can only be assessed theoretically. The theoretical analysis shows that the LWP error is constant with 20 g m−2 for LWP below 100 g m−2. While the absolute LWP error increases with increasing LWP, the relative one decreases from 20 % at 100 g m−2 to 10 % at 500 g m−2. The identification of clear-sky scenes by ancillary measurements, here backscatter lidar, is crucial for thin clouds (LWP < 12 g m−2) as the microwave retrieved LWP uncertainty is higher than 100 %. The analysis of both campaigns reveals that clouds were more frequent (47 % vs. 30 % of the time) in the dry than in the wet season. Their average LWP (63 vs. 40 g m−2) and RWP (6.7 vs. 2.7 g m−2) were higher as well. Microwave scattering of ice, however, was observed less frequently in the dry season (0.5 % vs. 1.6 % of the time). We hypothesize that a higher degree of cloud organization on larger scales in the wet season reduces the overall cloud cover and observed LWP. As to be expected, the observed IWV clearly shows that the dry season is on average less humid than the wet season (28 vs. 41 kg m−2). The results reveal that the observed frequency distributions of IWV are substantially affected by the choice of the flight pattern. This should be kept in mind when using the airborne observations to carefully mediate between long-term ground-based and spaceborne measurements to draw statistically sound conclusions.

Quantifying the effects of freeze-thaw transitions and snowpack melt on land surface albedo and energy exchange over Alaska and Western Canada

Variations in land surface albedo and snow-cover strongly impact the global biosphere, particularly through the snow-albedo feedback on climate. The seasonal freeze-thaw (FT) transition is coupled with snowpack melt dynamics and strongly impacts surface water mobility and the energy budget in the northern (≥45°N) arctic and boreal region (ABR). However, understanding of the regional variation in snowmelt and its effect on the surface energy budget are limited due to sparse in situ measurements of these processes and environmental constraints on effective monitoring within the ABR. In this study, we combined synergistic observations from overlapping satellite optical-infrared and microwave sensor records to quantify the regional patterns and seasonal progression in wet snow conditions during the spring snowmelt and autumn snow accumulation periods across Alaska and western Canada. The integrated satellite record included daily landscape FT status from AMSR microwave brightness temperature retrievals; and snow-cover extent, black sky albedo and net shortwave solar radiation (Rsnet) derived from MODIS and AVHRR observations. The integrated satellite records were analyzed with in situ surface air temperature and humidity observations from regional weather stations over a two-year study period (2015–2016) overlapping with the NASA ABoVE (Arctic Boreal Vulnerability Experiment). Our results show a large (79%) mean decline in land surface albedo between dry snow and snow-free conditions during the spring (March–June) and autumn (August–November) transition periods. Onset of diurnal thawing and refreezing of the surface snow layer and associated wet snow conditions in spring contributed to an approximate 25% decrease in snow cover albedo that extended over a seven to 21 week snowpack depletion period. The lower wet snow albedo enhances Rsnet by approximately 74% (9–10 MJ m−2 d−1) relative to dry snow conditions, reinforcing snowmelt and surface warming, and contributing to growing season onset and activation of biological and hydrological processes in the ABR. These results contribute to better understanding of snow albedo feedbacks to Arctic amplification, and the representation of these processes in global Earth system models.

Four decades of microwave satellite soil moisture observations: Part 1. A review of retrieval algorithms

The satellite based passive (radiometer) and active (radar) microwave sensors enhanced our ability to retrieve soil moisture at global scales. It has been almost four decades since the first passive microwave satellite sensor was launched in 1978. Since then soil moisture has gained considerable attention in hydro-meteorological, climate, and agricultural research resulting in the deployment of two dedicated missions in the last decade, SMOS and SMAP. Microwave retrievals require an algorithm to estimate soil moisture from satellite measurements. In this Part 1 of a two-part review series, we provide a synthesis of four decades of research and development on the passive and active microwave soil moisture retrieval algorithms. The algorithms associated with passive sensors use the radiometer brightness temperatures, while active sensors use the radar backscatter measurements to retrieve soil moisture. The physics of both algorithm classes are based on the fact that the microwave measurements at lower frequencies are influenced by the soil dielectric property, which acts as a proxy for the surface soil moisture content. In this review effort, the emphasis is laid on the physical models of the passive and the active retrieval algorithms. These algorithms facilitate to obtain the individual radiative contributions from soil, vegetation, and atmosphere that reach satellite sensors after mixing (roughness), scattering, and attenuation. In the process, we looked into the current research efforts to improve individual aspects of the algorithms, followed by a description of different retrieval procedures. In Part 2 of this review series, performance evaluation and inter-sensor comparisons of soil moisture of eight passive and two active sensors are carried out using 1058 stations along with model soil moisture data in the Contiguous United States (CONUS) region. (C) 2017 Elsevier Ltd. All rights reserved.

Improvement of AMSR2 Soil Moisture Products Over South Korea

Soil moisture (SM) is a critical parameter for interpreting the status of land surfaces and vegetation because it has an important role in the exchange of water and heat energy between the land surface and atmosphere. SM can be retrieved with satellite microwave sensors, which offer several advantages in that they are cost-effective, give rapid results, and provide data for inaccessible or isolated areas. In addition, these sensors cover large areas and perform periodic multi-channel observations. The advanced microwave scanning radiometer-2 (AMSR2), a successor of Advanced Microwave Scanning Radiometer for the Earth Observing System, provides regular global SM data products, which the Korea Meteorological Administration uses to calculate daily mean and seven-day moving window SM do not agree well with ground measurements in South Korea, and must be corrected for reliable drought monitoring. In this study, we used Global Land Data Assimilation System SM data to calibrate AMSR2 SM. In addition, we applied and combined two methods to correct AMSR2 SM: 1) linear regression with input variables, including insolation, daily precipitation, and the normalized difference vegetation index and 2) the cumulative distribution function (CDF) matching method. The results of the combined linear regression and CDF matching method were in better agreement with the in situ data than those of either method alone. Correcting AMSR2 SM by using the combined method improved the correlation coefficient (R) from 0.19 to 0.59 and the root-mean-square error from 0.23 to 0.09.

An Improved Local Gradient Method for Sea Surface Wind Direction Retrieval from SAR Imagery

Sea surface wind affects the fluxes of energy, mass and momentum between the atmosphere and ocean, and therefore regional and global weather and climate. With various satellite microwave sensors, sea surface wind can be measured with large spatial coverage in almost all-weather conditions, day or night. Like any other remote sensing measurements, sea surface wind measurement is also indirect. Therefore, it is important to develop appropriate wind speed and direction retrieval models for different types of microwave instruments. In this paper, a new sea surface wind direction retrieval method from synthetic aperture radar (SAR) imagery is developed. In the method, local gradients are computed in frequency domain by combining the operation of smoothing and computing local gradients in one step to simplify the process and avoid the difference approximation. This improved local gradients (ILG) method is compared with the traditional two-dimensional fast Fourier transform (2D FFT) method and local gradients (LG) method, using interpolating wind directions from the European Centre for Medium-Range Weather Forecast (ECMWF) reanalysis data and the Cross-Calibrated Multi-Platform (CCMP) wind vector product. The sensitivities to the salt-and-pepper noise, the additive noise and the multiplicative noise are analyzed. The ILG method shows a better performance of retrieval wind directions than the other two methods.

Simulation of Ship-Track versus Satellite-Sensor Differences in Oceanic Precipitation Using an Island-Based Radar

The point-to-area problem strongly complicates the validation of satellite-based precipitation estimates, using surface-based point measurements. We simulate the limited spatial representation of light-to-moderate oceanic precipitation rates along ship tracks with respect to areal passive microwave satellite estimates using data from a subtropical island-based radar. The radar data serves to estimate the discrepancy between point-like and areal precipitation measurements. From the spatial discrepancy, two statistical adjustments are derived so that along-track precipitation ship data better represent areal precipitation estimates from satellite sensors. The first statistical adjustment uses the average duration of a precipitation event as seen along a ship track, and the second adjustment uses the median-normalized along-track precipitation rate. Both statistical adjustments combined reduce the root mean squared error by 0.24 mm h − 1 (55%) compared to the unadjusted average track of 60 radar pixels in length corresponding to a typical ship speed of 24–34 km h − 1 depending on track orientation. Beyond along-track averaging, the statistical adjustments represent an important step towards a more accurate validation of precipitation derived from passive microwave satellite sensors using point-like along-track surface precipitation reference data.

Evaluating and Extending the Ocean Wind Climate Data Record.

Satellite microwave sensors, both active scatterometers and passive radiometers, have been systematically measuring near-surface ocean winds for nearly 40 years, establishing an important legacy in studying and monitoring weather and climate variability. As an aid to such activities, the various wind datasets are being intercalibrated and merged into consistent climate data records (CDRs). The ocean wind CDRs (OW-CDRs) are evaluated by comparisons with ocean buoys and intercomparisons among the different satellite sensors and among the different data providers. Extending the OW-CDR into the future requires exploiting all available datasets, such as OSCAT-2 scheduled to launch in July 2016. Three planned methods of calibrating the OSCAT-2 σo measurements include 1) direct Ku-band σo intercalibration to QuikSCAT and RapidScat; 2) multisensor wind speed intercalibration; and 3) calibration to stable rainforest targets. Unfortunately, RapidScat failed in August 2016 and cannot be used to directly calibrate OSCAT-2. A particular future continuity concern is the absence of scheduled new or continuation radiometer missions capable of measuring wind speed. Specialized model assimilations provide 30-year long high temporal/spatial resolution wind vector grids that composite the satellite wind information from OW-CDRs of multiple satellites viewing the Earth at different local times.

All-sky radiance simulation of Megha-Tropiques SAPHIR microwave sensor using multiple scattering radiative transfer model for data assimilation applications

Incorporation of cloud- and precipitation-affected radiances from microwave satellite sensors in data assimilation system has a great potential in improving the accuracy of numerical model forecasts over the regions of high impact weather. By employing the multiple scattering radiative transfer model RTTOV-SCATT, all-sky radiance (clear sky and cloudy sky) simulation has been performed for six channel microwave SAPHIR (Sounder for Atmospheric Profiling of Humidity in the Inter-tropics by Radiometry) sensors of Megha-Tropiques (MT) satellite. To investigate the importance of cloud-affected radiance data in severe weather conditions, all-sky radiance simulation is carried out for the severe cyclonic storm ‘Hudhud’ formed over Bay of Bengal. Hydrometeors from NCMRWF unified model (NCUM) forecasts are used as input to the RTTOV model to simulate cloud-affected SAPHIR radiances. Horizontal and vertical distribution of all-sky simulated radiances agrees reasonably well with the SAPHIR observed radiances over cloudy regions during different stages of cyclone development. Simulated brightness temperatures of six SAPHIR channels indicate that the three dimensional humidity structure of tropical cyclone is well represented in all-sky computations. Improved correlation and reduced bias and root mean square error against SAPHIR observations are apparent. Probability distribution functions reveal that all-sky simulations are able to produce the cloud-affected lower brightness temperatures associated with cloudy regions. The density scatter plots infer that all-sky radiances are more consistent with observed radiances. Correlation between different types of hydrometeors and simulated brightness temperatures at respective atmospheric levels highlights the significance of inclusion of scattering effects from different hydrometeors in simulating the cloud-affected radiances in all-sky simulations. The results are promising and suggest that the inclusion of multiple scattering radiative transfer models into data assimilation system can simulate the cloud-affected microwave radiance data which provide detailed information on three dimensional humidity structure of the atmosphere in the presence of cloud hydrometeors.

Multi-Decadal Variability of Polynya Characteristics and Ice Production in the North Water Polynya by Means of Passive Microwave and Thermal Infrared Satellite Imagery

The North Water (NOW) Polynya is a regularly-forming area of open-water and thin-ice, located between northwestern Greenland and Ellesmere Island (Canada) at the northern tip of Baffin Bay. Due to its large spatial extent, it is of high importance for a variety of physical and biological processes, especially in wintertime. Here, we present a long-term remote sensing study for the winter seasons 1978/1979 to 2014/2015. Polynya characteristics are inferred from (1) sea ice concentrations and brightness temperatures from passive microwave satellite sensors (Advanced Microwave Scanning Radiometer (AMSR-E and AMSR2), Scanning Multichannel Microwave Radiometer (SMMR), Special Sensor Microwave Imager/Sounder (SSM/I-SSMIS)) and (2) thin-ice thickness distributions, which are calculated using MODIS ice-surface temperatures and European Center for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis data in a 1D thermodynamic energy-balance model. Daily ice production rates are retrieved for each winter season from 2002/2003 to 2014/2015, assuming that all heat loss at the ice surface is balanced by ice growth. Two different cloud-cover correction schemes are applied on daily polynya area and ice production values to account for cloud gaps in the MODIS composites. Our results indicate that the NOW polynya experienced significant seasonal changes over the last three decades considering the overall frequency of polynya occurrences, as well as their spatial extent. In the 1980s, there were prolonged periods of a more or less closed ice cover in northern Baffin Bay in winter. This changed towards an average opening on more than 85% of the days between November and March during the last decade. Noticeably, the sea ice cover in the NOW polynya region shows signs of a later-appearing fall freeze-up, starting in the late 1990s. Different methods to obtain daily polynya area using passive microwave AMSR-E/AMSR2 data and SSM/I-SSMIS data were applied. A comparison with MODIS data (thin-ice thickness ≤ 20 cm) shows that the wintertime polynya area estimates derived by MODIS are about 30 to 40% higher than those derived using the polynya signature simulation method (PSSM) with AMSR-E data. In turn, the difference in polynya area between PSSM and a sea ice concentration (SIC) threshold of 70% is fairly low (approximately 10%) when applied to AMSR-E data. For the coarse-resolution SSM/I-SSMIS data, this difference is much larger, particularly in November and December. Instead of a sea ice concentration threshold, the PSSM method should be used for SSM/I-SSMIS data. Depending on the type of cloud-cover correction, the calculated ice production based on MODIS data reaches an average value of 264.4 ± 65.1 km 3 to 275.7 ± 67.4 km 3 (2002/2003 to 2014/2015) and shows a high interannual variability. Our achieved long-term results underline the major importance of the NOW polynya considering its influence on Arctic ice production and associated atmosphere/ocean processes.

Validation and trend analysis of ECV soil moisture data on cropland in North China Plain during 1981–2010

Global time series of the Essential Climate Variable (ECV) soil mositure (SM) is being developed from passive and active satellite microwave sensors at a coarse spatial resolution with Climate Change Initiative program funded by European Space Agency. This study aims to validate the reliability of ECV SM dataset, and attempts are made to analyze SM trends in cropland. Firstly, in-situ SM measurements during crop growing seasons from 1992 to 2010 for 228 stations across China and 21 stations over cropland of North China Plain (NCP) were employed to validate ECV SM product. Then, the spatiotemporal variations of ECV SM were analyzed during growing period of winter wheat (April–June) and summer maize (July–September) from 1981 to 2010 in NCP. Finally, the possible relationship between SM, precipitation, evapotranspiration and NDVI were explored. Results showed that ECV SM could generally capture the seasonal SM dynamics. The average triple collocation random error of ECV SM in China was 0.052m3m−3 while the error in cropland ranged from 0.003 to 0.156m3m−3. The averaged Spearman correlation coefficient between ECV SM and all in-situ observations was 0.42 (p<0.01) in China and 0.43 (p<0.01) for cropland over NCP. Spatially, ECV SM was decreasing in most areas during wheat season, whereas the trends of ECV SM were positive in south and negative in north during maize season in NCP, being consistent with the precipitation fluctuation. Overall, ECV SM is potentially suitable for trend analysis in NCP and its validations and analysis will be helpful for further enhancement of the ECV SM product.

The Impact of Local Acquisition Time on the Accuracy of Microwave Surface Soil Moisture Retrievals over the Contiguous United States

Satellite-derived soil moisture products have become an important data source for the study of land surface processes and related applications. For satellites with sun-synchronous orbits, these products are typically derived separately for ascending and descending overpasses with different local acquisition times. Moreover, diurnal variations in land surface conditions, and the extent to which they are accurately characterized in retrieval algorithms, lead to distinct systematic and random error characteristics in ascending versus descending soil moisture products. Here, we apply two independent evaluation techniques (triple collocation and direct comparison against sparse ground-based observations) to quantify (correlation-based) accuracy differences in satellite-derived surface soil moisture acquired at different local acquisition times. The orbits from different satellites are separated into two overpass categories: AM (12:00 a.m. to 11:59 a.m. Local Solar Time) and PM (12:00 p.m. to 11:59 p.m. Local Solar Time). Results demonstrate how patterns in the accuracy of AM versus PM retrieval products obtained from a variety of active and passive microwave satellite sensors vary according to land cover and across satellite products with different local acquisition times.

A Methodology to Determine Radio-Frequency Interference in AMSR2 Observations

A study to determine radio-frequency interference (RFI) in low-frequency passive microwave observations of the Advanced Microwave Scanning Radiometer-2 (AMSR2) is performed. RFI detection methods, such as the spectral difference method, have already been applied on microwave satellite sensors. However, these methods may result in false RFI detection, particularly in zones with extreme environmental conditions. To overcome this problem, this paper proposes an approach that uses the additional 7.3-GHz channel of the AMSR2 sensor in a new RFI detection method. This method uses calculated standard errors of estimate to detect RFI contamination in 6.9- and 7.3-GHz observations. It was found that 6.9-GHz observations are mainly contaminated in the USA, India, Japan, and parts of Europe. The 7.3-GHz observations are contaminated in South America, Ukraine, the Middle East, Southeast Asia, and Russia. The fact that these channels are not affected by RFI in exactly the same regions is useful for studies that prefer C-band brightness temperature observations (e.g., soil moisture retrieval algorithms). Therefore, a decision tree approach was set up to determine RFI and to select reliable brightness temperature observations in the lowest frequency free of any man-made contamination. The result is a reduction of the total contaminated pixels in the 6.9-GHz observations of 66% for horizontal observations and even 85% for vertical observations when 7.3 and 10.7 GHz are used. By linking RFI maps with civilization maps, this paper further shows that RFI sources at the C-band frequency are mainly located in urbanized areas.

Pan-Arctic lead detection from MODIS thermal infrared imagery

AbstractPolynyas and leads are key elements of the wintertime Arctic sea-ice cover. They play a crucial role in surface heat loss, potential ice formation and consequently in the seasonal sea-ice budget. While polynyas are generally sufficiently large to be observed with passive microwave satellite sensors, the monitoring of narrow leads requires the use of data at a higher spatial resolution. We apply and evaluate different lead segmentation techniques based on sea-ice surface temperatures as measured by the Moderate Resolution Imaging Spectroradiometer (MODIS). Daily lead composite maps indicate the presence of cloud artifacts that arise from ambiguities in the segmentation process and shortcomings in the MODIS cloud mask. A fuzzy cloud artifact filter is hence implemented to mitigate these effects and the associated potential misclassification of leads. The filter is adjusted with reference data from thermal infrared image sequences, and applied to daily MODIS data from January to April 2008. The daily lead product can be used to deduct the structure and dynamics of wintertime sea-ice leads and to assess seasonal divergence patterns of the Arctic Ocean.

Ice cloud detection from AMSU-A, MHS, and HIRS satellite instruments inferred by cloud profiling radar

An algorithm for ice cloud detection aided by support vector machine (AID-SVM) is presented. The AID-SVM algorithm is applied and tested for the Advanced Microwave Sounding Unit-A, microwave humidity sounder (MHS), and high resolution infrared radiation sounder (HIRS) instruments onboard NOAA-19 satellite. The algorithm is based on satellite brightness temperature measurements and developed as well as validated by using collocated ice/no-ice cloud information acquired from the CloudSat cloud-profiling radar. The algorithm is tested over both ocean and land surfaces. Overall, the results exhibit very promising potential to acquire ice/no-ice cloud information using the passive satellite sensors. It is found that infrared satellite sensor such as HIRS is more efficient in detecting ice clouds than the counterpart microwave satellite sensors. Furthermore, the combined measurements using microwave/infrared synergy perform no better than the infrared-only measurements.

The emission and scattering of L‐band microwave radiation from rough ocean surfaces and wind speed measurements from the Aquarius sensor

AbstractIn order to achieve the required accuracy in sea surface salinity (SSS) measurements from L‐band radiometers such as the Aquarius/SAC‐D or SMOS (Soil Moisture and Ocean Salinity) mission, it is crucial to accurately correct the radiation that is emitted from the ocean surface for roughness effects. We derive a geophysical model function (GMF) for the emission and backscatter of L‐band microwave radiation from rough ocean surfaces. The analysis is based on radiometer brightness temperature and scatterometer backscatter observations both taken on board Aquarius. The data are temporally and spatially collocated with wind speeds from WindSat and F17 SSMIS (Special Sensor Microwave Imager Sounder) and wind directions from NCEP (National Center for Environmental Prediction) GDAS (Global Data Assimilation System). This GMF is the basis for retrieval of ocean surface wind speed combining L‐band H‐pol radiometer and HH‐pol scatterometer observations. The accuracy of theses combined passive/active L‐band wind speeds matches those of many other satellite microwave sensors. The L‐band GMF together with the combined passive/active L‐band wind speeds is utilized in the Aquarius SSS retrieval algorithm for the surface roughness correction. We demonstrate that using these L‐band wind speeds instead of NCEP wind speeds leads to a significant improvement in the SSS accuracy. Further improvements in the roughness correction algorithm can be obtained by adding VV‐pol scatterometer measurements and wave height (WH) data into the GMF.

Global Precipitation Measurement (GPM) Ground Validation (GV) Prototype in the Korean Peninsula

Abstract Since 2009, the Korea Meteorological Administration (KMA) has participated in ground validation (GV) projects through international partnerships within the framework of the Global Precipitation Measurement (GPM) Mission. The goal of this work is to assess the reliability of ground-based measurements in the Korean Peninsula as a means for validating precipitation products retrieved from satellite microwave sensors, with an emphasis on East Asian precipitation. KMA has a well-developed operational weather service infrastructure composed of meteorological radars, a dense rain gauge network, and automated weather stations. Measurements from these systems, including data from four ground-based radars (GRs), were combined with satellite data from the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and used as a proxy for GPM GV over the Korean Peninsula. A time series of mean reflectivity differences (GR − PR) for stratiform-only and above-brightband-only data showed that the time-averaged difference fell between −2.0 and +1.0 dBZ for the four GRs used in this study. Site-specific adjustments for these relative mean biases were applied to GR reflectivities, and detailed statistical comparisons of reflectivity and rain rate between PR and bias-adjusted GR were carried out. In rain-rate comparisons, surface rain from the TRMM Microwave Imager (TMI) and the rain gauges were added and the results varied according to rain type. Bias correction has had a positive effect on GR rain rate comparing with PR and gauge rain rates. This study confirmed advance preparation for GPM GV system was optimized on the Korean Peninsula using the official framework.

Evaluation of a Global Soil Moisture Product from Finer Spatial Resolution SAR Data and Ground Measurements at Irish Sites

In the framework of the European Space Agency Climate Change Initiative, a global, almost daily, soil moisture (SM) product is being developed from passive and active satellite microwave sensors, at a coarse spatial resolution. This study contributes to its validation by using finer spatial resolution ASAR Wide Swath and in situ soil moisture data taken over three sites in Ireland, from 2007 to 2009. This is the first time a comparison has been carried out between three sets of independent observations from different sensors at very different spatial resolutions for such a long time series. Furthermore, the SM spatial distribution has been investigated at the ASAR scale within each Essential Climate Variable (ECV) pixel, without adopting any particular model or using a densely distributed network of in situ stations. This approach facilitated an understanding of the extent to which geophysical factors, such as soil texture, terrain composition and altitude, affect the retrieved ECV SM product values in temperate grasslands. Temporal and spatial variability analysis provided high levels of correlation (p &lt; 0.025) and low errors between the three datasets, leading to confidence in the new ECV SM global product, despite limitations in its ability to track the driest and wettest conditions.

Long-term variation in the North Pacific using satellite-derived wind data set/J-OFURO over the last decade and other data sets over a longer record

Time series of gridded data sets of surface winds (from Qscat (J-OFURO)) constructed by satellite microwave sensors covering almost a decade (1999–2009) are used to examine long-term change in surface wind fields over the world’s oceans. Evidence has been provided by most previous studies that wind speeds have a tendency to increase over time in many area, and we verify whether or not this tendency persists. Results reveal that zonal winds tended to be weaker over the study period in the region of the North Pacific where westerly winds prevail. Time series of different types of data sets based on numerical model products and voluntary ship measurements present similar features of weakening westerly winds, even allowing for discrepancies among the data sets. These time series also exhibit a tendency of enhanced westerly winds in periods prior to the start of the twenty-first century, which means that the long-term trend in wind speed has changed from positive in the 1980s/1990s to negative in the 2000s. Examinations of time series for each season reveal that the above feature is found in winter, suggesting that it is related to the strength of the Aleutian Low.

Soil as a natural rain gauge: Estimating global rainfall from satellite soil moisture data

AbstractMeasuring precipitation intensity is not straightforward; and over many areas, ground observations are lacking and satellite observations are used to fill this gap. The most common way of retrieving rainfall is by addressing the problem “top‐down” by inverting the atmospheric signals reflected or radiated by atmospheric hydrometeors. However, most applications are interested in how much water reaches the ground, a problem that is notoriously difficult to solve from a top‐down perspective. In this study, a novel “bottom‐up” approach is proposed that, by doing “hydrology backward,” uses variations in soil moisture (SM) sensed by microwave satellite sensors to infer preceding rainfall amounts. In other words, the soil is used as a natural rain gauge. Three different satellite SM data sets from the Advanced SCATterometer (ASCAT), the Advanced Microwave Scanning Radiometer (AMSR‐E), and the Microwave Imaging Radiometer with Aperture Synthesis are used to obtain three new daily global rainfall products. The “First Guess Daily” product of the Global Precipitation Climatology Centre (GPCC) is employed as main benchmark in the validation period 2010–2011 for determining the continuous and categorical performance of the SM‐derived rainfall products by considering the 5 day accumulated values. The real‐time version of the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis product, i.e., the TRMM‐3B42RT, is adopted as a state‐of‐the‐art satellite rainfall product. The SM‐derived rainfall products show good Pearson correlation values (R) with the GPCC data set, mainly in areas where SM retrievals are found to be accurate. The global median R values (in the latitude band ±50°) are equal to 0.54, 0.28, and 0.31 for ASCAT‐, AMSR‐E‐, and SMOS‐derived products, respectively. For comparison, the median R for the TRMM‐3B42RT product is equal to 0.53. Interestingly, the SM‐derived products are found to outperform TRMM‐3B42RT in terms of average global root‐mean‐square error statistics and in terms of detection of rainfall events. The regions for which the SM‐derived products perform very well are Australia, Spain, South and North Africa, India, China, the Eastern part of South America, and the central part of the United States. The SM‐derived products are found to estimate accurately the rainfall accumulated over a 5 day period, an aspect particularly important for their use for hydrological applications, and that address the difficulties of estimating light rainfall from TRMM‐3B42RT.