Passive Microwave Imagery Research Articles

Evaluation of Sea Ice Motion Estimates from Enhanced Resolution Passive Microwave Brightness Temperatures

Sea ice motion plays an important role in the seasonal and interannual evolution of the polar sea ice cover. Satellite imagery can be used to track the motion of sea ice via cross-correlation feature tracking algorithms. Such a method has been used for the National Snow and Ice Data Center (NSIDC) sea ice motion product, based largely on passive microwave imagery. This study investigates the use of a new enhanced resolution passive microwave brightness temperature (TB) product to derive ice motion products. The results demonstrate that the new imagery source provides useful daily motion estimates that provide denser spatial coverage and reduced errors. The enhanced TBs yield motions that have a 30% lower Root Mean Square (RMS) difference with motion estimates from buoys. The enhanced resolution TBs will be used in the new version of the NSIDC motion product that is currently in development.

Satellite based analysis of rapid intensification of Super Cyclone Amphan

Tropical Cyclones (TCs) are formidable natural hazards with destructive impacts on life and property along the tropical belt. TCs are responsible for multiple hazards such as riverine and urban floods, extreme winds, and lightning, storm surge significantly amplifying the threats they pose. Rapid Intensification (RI), defined as a sudden increase in Maximum Sustained Winds (MSWs) by 30 kt or more in 24 h, has been a growing concern. These events, characterized by a swift escalation in MSW are challenging to the forecasters and remain a considerable operational challenge, amplifying risks for coastal communities. Numerical modeling also struggles to predict RI, with limitations in describing inner core convective scale processes cited as a major limitation. Monitoring and assessing TCs heavily rely on satellite-based observations due to the absence of adequate in-situ measurements over the ocean. In the present study, we examine Super Cyclonic Storm (SuCS) Amphan, the first Super Cyclone of this century, that formed over the Bay of Bengal in 2020 using satellite imagery and derived products available in near-real time to monitor and identify the early signatures for the RI. It is observed that the intensification is intricately tied to substantial expansions in both the outer and inner core sizes along with its symmetricity before the RI. Notably, no significant alterations in size are observed between the very severe cyclonic storm (VSCS) and SuCS stages. The convection characteristics of TCs are found to provide crucial markers of their intensification, with a particular emphasis on polar satellite-based passive microwave imagery for analyzing embedded convection and early eye detection. Key observations indicate that inner convective ring patterns, identified through 37Ghz microwave imagery in lower-level convection, act as precursors to rapid intensification. Concurrently, the outer ring pattern observed during the mid-life course is found to permit further intensification of the system.

Documenting the Progressions of Secondary Eyewall Formations

Abstract Intense tropical cyclones can form secondary eyewalls (SEs) that contract toward the storm center and eventually replace the inner eyewall, a process known as an eyewall replacement cycle (ERC). However, SE formation does not guarantee an eventual ERC, and often, SEs follow differing evolutionary pathways. This study documents SE evolution and progressions observed in numerous tropical cyclones, and results in two new datasets using passive microwave imagery: a global subjectively labeled dataset of SEs and eyes and their uncertainties from 72 storms between 2016 and 2019, and a dataset of 87 SE progressions that highlights the broad convective organization preceding and following an SE formation. The results show that two primary SE pathways exist: “No Replacement,” known as “Path 1,” and “Replacement,” known as the “Classic Path.” Most interestingly, 53% of the most certain SE formations result in an eyewall replacement. The Classic Path is associated with stronger column average meridional wind, a faster poleward component of storm motion, more intense storms, weaker vertical wind shear, greater relative humidity, a larger storm wind field, and stronger cold-air advection. This study highlights that a greater number of potential SE pathways exist than previously thought. The results of this study detail several observational features of SE evolution that raise questions about the physical processes that drive SE formations. Most important, environmental conditions and storm metrics identified here provide guidance for predictors in artificial intelligence applications for future tropical cyclone SE detection algorithms.

A climate data record of year-round global sea-ice drift from the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI SAF)

Abstract. Sea ice in the polar regions can move several tens of kilometres per day under the actions of winds, ocean currents, and internal stresses. Long-term observations of the rate and patterns of this motion are needed to characterize the full response of the polar environment to climate change. Here, we introduce a new climate data record (CDR) of year-round, global, daily sea-ice drift vectors covering 1991–2020. The motion vectors are computed from series of passive microwave imagery in the winter months and from a parametric free-drift model in the summer months. An evaluation against on-ice buoy trajectories reveals that the RMSEs of the sea-ice drift CDR are small and vary with hemisphere and seasons (2.1 km for Arctic winters, 2.6 km for Arctic summer, 3 to 4 km for the Antarctic sea ice). The CDR is un-biased for Arctic winter conditions. The bias is larger for Antarctic and for summer sea-ice motion. The CDR consists of daily product files holding the dX and dY components of the drift vectors on an Equal-Area Scalable Earth (EASE2) grid with 75 km spacing as well as associated uncertainties and flags. It is prepared in the context of the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI SAF) and is readily available at https://doi.org/10.15770/EUM_SAF_OSI_0012 (OSI SAF, 2022).

Weekly mapping of surface water extent in the intertropical wetlands using spaceborne GNSS reflectometry

As of today, the uncertain distribution and dynamics of inundations in the tropical wetlands cause large uncertainties in the quantification of the world’s methane emissions. Long-term, global remote sensing observations currently rely on optical sensors, affected by cloud cover and vegetation, and on passive microwave imagery with a low spatial resolution. Global Navigation Satellite System Reflectometry (GNSS-R) L-band observations from space onboard the Cyclone GNSS (CYGNSS) mission can provide information about the dynamics of surface water at moderate to low spatial resolution and with a frequent revisit. In this article, we propose a methodology to compute the fraction of water contained in 0.1°pixels (∼10 km) using CYGNSS reflectivity and Above Ground Biomass (AGB) maps. We produced a 1-year time series of water fractions with a weekly time sampling for the full coverage of CYGNSS between ±38°of latitude, from August 2018 to July 2019. We evaluated the results against regional reference maps of surface water based on MODIS, JERS-1, and PALSAR, and against the Global Inundation Extent from Multi-Satellite (GIEMS) dataset. CYGNSS water fractions represent well the spatial distribution and the seasonality of inundation extent for most of the tropical wetlands, with a global RMSD of 15.0 % against GIEMS. We found a likely overestimation when the soil moisture (SM) exceeds 0.3-0.4 cm3/cm3, i.e. for saturated soils where GNSS-R reflections are coherent, as the SM was not parameterized in our retrieval model. Despite this, the results are consistent in the entire intertropical band including over densely vegetated areas. These weekly water fractions for 2018-2019 with a 0.1°pixel size are distributed to contribute to further comparisons and hydrological researches.

M-PERC: A New Satellite Microwave-Based Model to Diagnose the Onset of Tropical Cyclone Eyewall Replacement Cycles

Abstract Eyewall replacement cycles (ERCs) in tropical cyclones (TCs) are generally associated with rapid changes in TC wind intensity and broadening of the TC wind field, both of which can create unique forecasting challenges. As part of the NOAA Joint Hurricane Testbed Project, a new model was developed to provide operational probabilistic guidance on ERC onset. The model is based on the time evolution of TC wind intensity and passive satellite microwave imagery and is named “M-PERC” for Microwave-Based Probability of Eyewall Replacement Cycle. The model was initially developed in the Atlantic basin but is found to be globally applicable and skillful. The development of M-PERC and its performance characteristics are described here, as well as a new intensity prediction model that extends previous work. Application of these models is expected to contribute to a reduction of TC intensity forecast error.

Spectral Linear Mixing Model application in passive microwave data to analyze Antarctic surface melting dynamics (1978-2018).

Several studies have utilized passive microwave imagery for monitoring snowmelt in Antarctica. However, due to the low spatial resolution of these images (25 km), the quantification of snowmelt is not precise. To enhance the accuracy of these estimations, this study proposed a subpixel analysis approach based on a Spectral Linear Mixing Model. This approach was applied to images obtained from channels 18/19 GHz and 37 GHz, both horizontally and vertically polarized, acquired from the Scanning Multichannel Microwave Radiometer (SMMR), Special Sensor Microwave Imager (SSM/I), and Special Sensor Microwave Imager/Sounder (SSM/IS) instruments, spanning the period 1978-2018. The spatiotemporal analysis of the estimated snowmelt fraction images indicated that the most persistent and intensive melt was observed on the Antarctic Peninsula, particularly on the Larsen, Wilkins, George VI, and Wordie ice shelves. The melting period in the Antarctic Peninsula began in late October, with a peak in early January, and ended in late March. Other regions with persistent and intensive snowmelt were Mary Bird Land and Wilkes Land, followed by Dronning Maud Land, Amery Ice Shelf, Filchner-Ronne Ice Shelf, and Ross Ice Shelf. These snowmelt data are valuable for modeling the impacts of snowmelt on glacial systems, local coastal environments, and sea-level rise.

Comparison of ARCHER MPERC to NHC Analysis

Eyewall replacement cycles (ERCs) are events that occur in intense tropical cyclones (TCs) and are difficult to predict. An ERC event involves a secondary outer eyewall that surrounds the inner eyewall. The outer eyewall slowly moves towards the eye and weakens the inner eyewall, eventually replacing the inner eyewall. During this process, wind speeds lower and the structure of a TC becomes disorganized, further weakening the storm. TCs often restrengthen after an ERC. Little is known about the process and as such, poses an obstacle to forecasters. The Automated Rotational Center Hurricane Eye Retrieval (ARCHER) Microwave-based Probability of Eyewall Replacement Cycle (MPERC) is an algorithm that uses 89-95 GHz passive microwave imagery and intensity estimates from the National Hurricane Center (NHC), Central Pacific Hurricane Center (CPHC), or the Joint Typhoon Warning Center (JTWC) to predict the possibility of an ERC. The effectiveness and ability of ARCHER MPERC was analyzed and compared to the NHC’s official reports on all Atlantic Basin tropical cyclones from 2017 to 2019. MPERC ultimately predicted seventeen ERCs in nine tropical cyclones. Of those, seven were valid ERCs. The algorithm works well, predicting approximately 41% of the total number of predictions correctly. However, MPERC did not predict five ERCs that were cited by the NHC. It was further found that it was true that MPERC produces incorrect results in sheared and dry environments.

Energetics of surface melt in West Antarctica

Abstract. We use reanalysis data and satellite remote sensing of cloud properties to examine how meteorological conditions alter the surface energy balance to cause surface melt that is detectable in satellite passive microwave imagery over West Antarctica. This analysis can detect each of the three primary mechanisms for inducing surface melt at a specific location: thermal blanketing involving sensible heat flux and/or longwave heating by optically thick cloud cover, all-wave radiative enhancement by optically thin cloud cover, and föhn winds. We examine case studies over Pine Island and Thwaites glaciers, which are of interest for ice shelf and ice sheet stability, and over Siple Dome, which is more readily accessible for field work. During January 2015 over Siple Dome we identified a melt event whose origin is an all-wave radiative enhancement by optically thin clouds. During December 2011 over Pine Island and Thwaites glaciers, we identified a melt event caused mainly by thermal blanketing from optically thick clouds. Over Siple Dome, those same 2011 synoptic conditions yielded a thermal-blanketing-driven melt event that was initiated by an impulse of sensible heat flux and then prolonged by cloud longwave heating. The December 2011 synoptic conditions also generated föhn winds at a location on the Ross Ice Shelf adjacent to the Transantarctic Mountains, and we analyze this case with additional support from automatic weather station data. In contrast, a late-summer thermal blanketing period over Pine Island and Thwaites glaciers during February 2013 showed surface melt initiated by cloud longwave heating and then prolonged by enhanced sensible heat flux. One limitation thus far with this type of analysis involves uncertainties in the cloud optical properties. Nevertheless, with improvements this type of analysis can enable quantitative prediction of atmospheric stress on the vulnerable Antarctic ice shelves in a steadily warming climate.

Testing Passive Microwave-Based Hail Retrievals Using GPM DPR Ku-Band Radar

AbstractSeveral studies in the literature have developed approaches to diagnose hail storms from satellite-borne passive microwave imagery and build nearly global climatologies of hail. This paper uses spaceborne Ku-band radar measurements from the Global Precipitation Measurement (GPM) mission Dual-Frequency Precipitation Radar (DPR) to validate several passive microwave approaches. We assess the retrievals on the basis of how tightly they constrain the radar reflectivity at −20°C and how this measured radar reflectivity aloft varies geographically. The algorithm that combines minimum 19-GHz polarization corrected temperature (PCT) with a 37-GHz PCT depression normalized by tropopause height constrains the radar reflectivity most tightly and gives the least appearance of regional biases. A retrieval that is based on a 19-GHz PCT threshold of 261K also produces tightly clustered profiles of radar reflectivity, with little regional bias. An approach using regionally adjusted minimum 37-GHz PCT performs relatively well, but our results indicate it may overestimate hail in some subtropical and midlatitude regions. A threshold applied to the minimum 37-GHz PCT (≤230 K), without any scaling by region or probability of hail, overestimates hail in the tropics and underestimates beyond the tropics. For all retrieval approaches, storms identified as having hail tended to have radar reflectivity profiles that are consistent with general expectations for hailstorms (reflectivity > 50 dBZ below the 0°C level, and > 40 dBZ extending far above 0°C). Profiles from oceanic regions tended to have more rapidly decreasing reflectivity with height than profiles from other regions. Subtropical, high-latitude, and high-terrain land profiles had the slowest decreases of reflectivity with height.

Snow depth retrieval from passive microwave imagery using different artificial neural networks

The high degree of nonlinearity in the analysis of hydrologic systems demonstrates that artificial neural networks are suitable methods for this purpose. Artificial neural networks and passive microwave imagery have been combined for monitoring snow parameters, particularly in arid and semi-arid areas where the hydrologic process of the water basin is very dependent on the snow conditions (snow depth, snow water equivalent, snow density, snow cover area, snow stratigraphy, the shape of the crystals of snow). The multilayer perceptron is learned by different methods (Levenberg-Marquardt, scaled conjugate gradient, gradient descent with momentum and adaptive learning rate). These multilayer perceptrons are compared with radial basis function and multilayer perceptron–genetic algorithm for evaluating snow depth. Snow depth is estimated using passive microwave brightness temperature of a special sensor microwave/imager sensor. The results indicate that multilayer perceptron–genetic algorithm outperformed other artificial neural networks (multilayer perceptron–Levenberg-Marquardt, multilayer perceptron–scaled conjugate gradient, multilayer perceptron–gradient descent with momentum and adaptive learning rate, multilayer perceptron–genetic algorithm and radial basis function) with r = 0.97, RMSE = 0.18, and NSE = 0.83 for training and r = 0.90, RMSE = 0.19, and NSE = 0.39 for testing.

Using Deep Learning to Estimate Tropical Cyclone Intensity from Satellite Passive Microwave Imagery

AbstractA deep learning convolutional neural network model is used to explore the possibilities of estimating tropical cyclone (TC) intensity from satellite images in the 37- and 85–92-GHz bands. The model, called “DeepMicroNet,” has unique properties such as a probabilistic output, the ability to operate from partial scans, and resiliency to imprecise TC center fixes. The 85–92-GHz band is the more influential data source in the model, with 37 GHz adding a marginal benefit. Training the model on global best track intensities produces model estimates precise enough to replicate known best track intensity biases when compared to aircraft reconnaissance observations. Model root-mean-square error (RMSE) is 14.3 kt (1 kt ≈ 0.5144 m s−1) compared to two years of independent best track records, but this improves to an RMSE of 10.6 kt when compared to the higher-standard aircraft reconnaissance-aided best track dataset, and to 9.6 kt compared to the reconnaissance-aided best track when using the higher-resolution TRMM TMI andAquaAMSR-E microwave observations only. A shortage of training and independent testing data for category 5 TCs leaves the results at this intensity range inconclusive. Based on this initial study, the application of deep learning to TC intensity analysis holds tremendous promise for further development with more advanced methodologies and expanded training datasets.

A Method to Determine the Margins of High Sea Ice Concentration Using AMSR-E Passive Microwave Imagery

The margin of the sea ice with high sea ice concentration is a principal feature in microwave image and a hotspot in image recognition. A method for determining the margins is developed using the feature of dual-polarized brightness temperatures at 36.5 GHz and a new parameter (contrast ratio) is used in this paper. For the microwaves, the ratio of the horizontal-polarized emissivity to the vertical-polarized emissivity is approximately equal to the ratio between horizontal-polarized and vertical-polarized brightness temperatures of sea surface, which called as the dual-polarized emissivity ratio in this study. It is found that the dual-polarized emissivity ratio of sea ice with nearly 100% sea ice concentration in Arctic at 36.5 GHz band has a value ranged between 0.92 and 0.96, as shown by satellite-observed data in figure of horizontal-po-larized brightness temperature versus vertical-polarized brightness temperature. From open water to sea ice covered area, the contrast-ratio can show the changing features of the dual-polarized brightness temperature at 36.5 GHz. The contrast ratio rapidly changes at the ice margins and its gradient appears an extreme value when the ratio changes around 0.92. This extreme value is examined by the ice concentration calculated by the MODIS data. And the results indicate that the threshold ratio coincides with the contour line of 96% sea ice concentration. So the parameter of contrast ratio could be used to determine the position of margins in microwave image.

Assimilation of ice and water observations from SAR imagery to improve estimates of sea ice concentration

In this paper, the assimilation of binary observations calculated from synthetic aperture radar (SAR) images of sea ice is investigated. Ice and water observations are obtained from a set of SAR images by thresholding ice and water probabilities calculated using a supervised maximum likelihood estimator (MLE). These ice and water observations are then assimilated in combination with ice concentration from passive microwave imagery for the purpose of estimating sea ice concentration. Due to the fact that the observations are binary, consisting of zeros and ones, while the state vector is a continuous variable (ice concentration), the forward model used to map the state vector to the observation space requires special consideration. Both linear and non-linear forward models were investigated. In both cases, the assimilation of SAR data was able to produce ice concentration analyses in closer agreement with image analysis charts than when assimilating passive microwave data only. When both passive microwave and SAR data are assimilated, the bias between the ice concentration analyses and the ice concentration from ice charts is 19.78%, as compared to 26.72% when only passive microwave data are assimilated. The method presented here for the assimilation of SAR data could be applied to other binary observations, such as ice/water information from visual/infrared sensors.

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.

Diagnosing Rain Occurrences Using Passive Microwave Imagery: A Comparative Study on Probabilistic Graphical Models and “Black Box” Models

AbstractRainfall is a fundamental process in the hydrologic cycle. This study investigated the cause–effect relationship in which precipitation at lower frequencies affects the amount of emitted radiation and at higher frequencies affects the amount of backscattered terrestrial radiation. Because the advantage of a probabilistic graphical model is its graphical representation, which allows easy causality interpretation using the arc directions, two Bayesian networks (BNs) were used, namely, a naïve Bayes classifier and a tree-augmented naïve Bayes model. To empirically evaluate and compare BN-based models, “black box”–based models, including nearest-neighbor searches and artificial neural network (ANN)-based multilayer perceptron and logistic regression, were used as benchmarks. For the two study regions—namely, the Tanshui River basin in northern Taiwan and Chianan Plain in southern Taiwan—rain occurrences during typhoon seasons were examined using passive microwave imagery recorded using the Special Sensor Microwave Imager/Sounder. The results show that although black box models exhibit excellent prediction ability, interpretation of their behavior is unsatisfactory. By contrast, probabilistic graphical models can explicitly reveal the causal relationship between brightness temperatures and nonrain/rain discrimination. For the Tanshui River basin, 19.35-, 22.23-, 37.0-, and 85.5-GHz vertically polarized brightness temperatures were found to diagnose rain occurrences. For the Chianan Plain, a more sensitive indicator of rain-scattering signals was obtained using 85-GHz measurements. The results demonstrate the potential use of BNs in identifying rain occurrences in regions with land features comprising various absorbing and scattering materials.

Assimilation of ice and water observations from SAR imagery to improve estimates of sea ice concentration

In this paper, the assimilation of binary observations calculated from synthetic aperture radar (SAR) images of sea ice is investigated. Ice and water observations are obtained from a set of SAR images by thresholding ice and water probabilities calculated using a supervised maximum likelihood estimator (MLE). These ice and water observations are then assimilated in combination with ice concentration from passive microwave imagery for the purpose of estimating sea ice concentration. Due to the fact that the observations are binary, consisting of zeros and ones, while the state vector is a continuous variable (ice concentration), the forward model used to map the state vector to the observation space requires special consideration. Both linear and non-linear forward models were investigated. In both cases, the assimilation of SAR data was able to produce ice concentration analyses in closer agreement with image analysis charts than when assimilating passive microwave data only. When both passive microwave and SAR data are assimilated, the bias between the ice concentration analyses and the ice concentration from ice charts is 19.78%, as compared to 26.72% when only passive microwave data are assimilated. The method presented here for the assimilation of SAR data could be applied to other binary observations, such as ice/water information from visual/infrared sensors. Keywords: sea ice, data assimilation, synthetic aperture radar, binary, Bayesian, probability, forward model (Published: 18 September 2015) Citation: Tellus A 2015, 67, 27218, http://dx.doi.org/10.3402/tellusa.v67.27218

Improvements in the Probabilistic Prediction of Tropical Cyclone Rapid Intensification with Passive Microwave Observations

Abstract The probabilistic prediction of tropical cyclone (TC) rapid intensification (RI) in the Atlantic and eastern Pacific Ocean basins is examined here using a series of logistic regression models trained on environmental and infrared satellite-derived features. The environmental predictors are based on averaged values over a 24-h period following the forecast time. These models are compared against equivalent models enhanced with additional TC predictors created from passive satellite microwave imagery (MI). Leave-one-year-out cross validation on the developmental dataset shows that the inclusion of MI-based predictors yields more skillful RI models for a variety of RI and intensity thresholds. Compared with the baseline forecast skill of the non-MI-based RI models, the relative skill improvements from including MI-based predictors range from 10.6% to 44.9%. Using archived real-time data during the period 2004–13, evaluation of simulated real-time models is also carried out. Unlike in the model development stage, the simulated real-time setting involves using Global Forecast System forecasts for the non-satellite-based predictors instead of “perfect” observational-based predictors in the developmental data. In this case, the MI-based RI models still generate superior skill to the baseline RI models lacking MI-based predictors. The relative improvements gained in adding MI-based predictors are most notable in the Atlantic, where the non-MI versions of the models suffer acutely from the use of imperfect real-time data. In the Atlantic, relative skill improvements provided from the inclusion of MI-based predictors range from 53.5% to 103.0%. The eastern Pacific relative improvements are less impressive but are still uniformly positive.

Evaluation of Satellite Rainfall Estimates for Drought and Flood Monitoring in Mozambique

Satellite derived rainfall products are useful for drought and flood early warning and overcome the problem of sparse, unevenly distributed and erratic rain gauge observations, provided their accuracy is well known. Mozambique is highly vulnerable to extreme weather events such as major droughts and floods and thus, an understanding of the strengths and weaknesses of different rainfall products is valuable. Three dekadal (10-day) gridded satellite rainfall products (TAMSAT African Rainfall Climatology And Time-series (TARCAT) v2.0, Famine Early Warning System NETwork (FEWS NET) Rainfall Estimate (RFE) v2.0, and Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS)) are compared to independent gauge data (2001–2012). This is done using pairwise comparison statistics to evaluate the performance in estimating rainfall amounts and categorical statistics to assess rain-detection capabilities. The analysis was performed for different rainfall categories, over the seasonal cycle and for regions dominated by different weather systems. Overall, satellite products overestimate low and underestimate high dekadal rainfall values. The RFE and CHIRPS products perform as good, generally outperforming TARCAT on the majority of statistical measures of skill. TARCAT detects best the relative frequency of rainfall events, while RFE underestimates and CHIRPS overestimates the rainfall events frequency. Differences in products performance disappear with higher rainfall and all products achieve better results during the wet season. During the cyclone season, CHIRPS shows the best results, while RFE outperforms the other products for lower dekadal rainfall. Products blending thermal infrared and passive microwave imagery perform better than infrared only products and particularly when meteorological patterns are more complex, such as over the coastal, central and south regions of Mozambique, where precipitation is influenced by frontal systems.

Comparison of the classification accuracy of three soil freeze–thaw discrimination algorithms in China using SSMIS and AMSR-E passive microwave imagery

This study compared the classification accuracies of three soil freeze–thaw discrimination algorithms in China using Special Sensor Microwave Imager/Sounder (SSMIS) and Advanced Microwave Scanning Radiometer Earth Observing System (AMSR-E) passive microwave imagery from 2008. The algorithms used were the dual-index algorithm (DIA), the decision tree algorithm (DTA), and the discriminant function algorithm (DFA). The comparison was conducted based on 0 cm land-surface temperature data from 756 meteorological stations across China by constructing error meta-matrices, and it is divided into two parts. The first part compared the overall classification accuracies from two aspects: temporal variation and spatial distribution. In the second part, the classification accuracies of frozen and thawed soils were evaluated. Results showed that both SSMIS and AMSR-E data can be applied to the DIA, DTA, and DFA algorithms, although they were originally developed from different satellite data sets. However, each of the three algorithms has its own advantages and disadvantages. Possible improvements in the three algorithms for future work are also discussed.