Rainfall retrieval over Indian land and oceanic regions from SSM/I microwave data

Present study focuses on the estimation of rainfall over Indian land and oceanic regions from the Special Sensor Microwave/Imager (SSM/I) on the Defense Meteorological Satellite Program (DMSP) F-13. Based on the measurements at 19.35, 22.235 and 85.5 GHz channels of SSM/I Satellite, scattering index (SI) has been developed for the Indian land and oceanic regions separately. These scattering indices were co-located against rainfall from Precipitation Radar (PR) onboard Tropical Rainfall Measuring Mission (TRMM) to develop a new regional relationship between the SI and the rain rate for the Indian land and oceanic regions. A non-linear fit between the rain rate and the SI is established for rain measurement. In order to have confidence in our method, we have also estimated rainfall using the global rainfall and scattering index relationship developed by Ferraro and Marks [Ferraro, R.R., Marks, G.F. The development of SSM/I rain rate retrieval algorithms using ground based radar measurements. J. Atmos. Ocean. Technol. 12, 755–770, 1995]. The validation with the rain-gauge shows that the present scheme is able to retrieve rainfall with better accuracy than that of Ferraro and Marks (1995). Further intercomparison with TRMM-2A12 and validation with rain-gauges rainfall showed that the present algorithm is able to retrieve the rainfall with reasonably good accuracy.     

An improved approach for rainfall estimation over Indian summer monsoon region using Kalpana-1 data

In this paper, an improved Kalpana-1 infrared (IR) based rainfall estimation algorithm, specific to Indian summer monsoon region is presented. This algorithm comprises of two parts: (i) development of Kalpana-1 IR based rainfall estimation algorithm with improvement for orographic warm rain underestimation generally suffered by IR based rainfall estimation methods and (ii) cooling index to take care of the growth and decay of clouds and thereby improving the precipitation estimation.     

A multilayer perceptron and multiclass support vector machine based high accuracy technique for daily rainfall estimation from MSG SEVIRI data

The current paper introduces a new multilayer perceptron (MLP) and support vector machine (SVM) based approach to improve daily rainfall estimation from the Meteosat Second Generation (MSG) data. In this study, the precipitation is first detected and classified into convective and stratiform rain by two MLP models, and then four multi-class SVM algorithms were used for daily rainfall estimation. Relevant spectral and textural input features of the developed algorithms were derived from the spectral MSG SEVIRI radiometer channels. The models were trained using radar rainfall data set colected over north Algeria. Validation of the proposed daily rainfall estimation technique was performed by rain gauge network data set recorded over north Algeria. Thus, several statistical scores were calculated, such as correlation coefficient (r), root mean square error (RMSE), mean error (Bias), and mean absolute error (MAE). The findings given by: (r = 0.97, bias = 0.31 mm, RMSE = 2.20 mm and MAE = 1.07 mm), showed a quite satisfactory relationship between the estimation and the respective observed daily precipitation. Moreover, the comparison of the results with those of two advanced techniques based on random forests (RF) and weighted ‘k’ nearest neighbor (WkNN) showed higher accuracy obtained by the proposed model.     

Spatial and temporal monitoring of drought conditions using the satellite rainfall estimates and remote sensing optical and thermal measurements

Drought is an important natural disaster that causes devastating impacts on the ecosystem, livestock, environment, and society. So far, various remote-sensing methods have been developed to estimate drought conditions, each of which has advantages and restrictions. This study aims to monitor the real-time drought indices at the field scales via the integration of various earth observations. Our proposed method consists of two steps. In the first step, the relationships between long-term standardized precipitation indices (SPI) derived from PERSIANN-CDR rainfall data and two drought-dependent parameters derived from MODIS products, including normalized NDVI and soil-air temperature gradient, are obtained at the spatial resolution of PERSIANN-CDR grid (approximately 25 km). As the next step, the corresponding relationships are applied to estimate the drought index maps at the spatial resolution of MODIS products (1 km). Numerous analyses are carried out to evaluate the proposed method. The results revealed that, from various drought indices, including SPIs of different timescales (1, 3, 6, and 12-months), SPI-3 and SPI-6 are more appropriate to the proposed method in terms of correlation with temperature and vegetation parameters. The findings also demonstrate the competency of the proposed method in estimating SPI indices with average RMSE 0.67 and the average correlation coefficient of 0.74.     

Lower-mesospheric inversion layers over brazilian equatorial region using TIMED/SABER temperature profiles

Lower-mesospheric inversion layers (MILs) were studied using the temperature profiles observed by TIMED/SABER over Cariri (7.5°S, 36.5°W), Brazil, in 2005. A total 175 MILs were identified with the maximum occurrence in April and October and the minimum in January and July. The lower MIL is located in a height region from 70 to 90 km, with the peak at around 83 ± 4 km with the temperature of 205 ± 5 K, and the thickness of 4–10 km. The results show large amplitudes of MILs during equinoxes and minimum in solstices, with a clear semiannual variation. A general feature of lower MIL in monthly mean profile was observed twice a year, one from February to May, and the other from August to October with a downward shift of the top level. These results suggest that formation and long persistence of MIL is an important factor to investigate propagation of atmospheric gravity waves in the mesosphere-lower thermosphere (MLT) region.     

Validation of the operational IASI level 2 processor using AIRS and ECMWF data

The algorithms being implemented in EUMETSAT’s IASI Level 2 Product Processing Facility are validated with real case situations using AIRS data and comparing the retrieved atmospheric states with ECMWF analyses. The tests have been performed for clear-sky ocean scenes during daytime.

The Empirical Orthogonal Function (EOF) retrievals show very good performance, with retrieved atmospheric states standard deviations between 1 and 2 K in temperature and 10% and 20% in relative humidity when compared with ECMWF analysis in the troposphere. The EOF retrievals show relatively smooth profiles.

Results from an iterative retrieval show a standard deviation between 2 and 3 K in temperature and 10% and 30% in relative humidity when compared with ECMWF analyses in the troposphere. They tend to show meteorologically reasonable discontinuities in both temperature and relative humidity. This seems to be the reason why they do not compare as well with ECMWF analyses as the EOF retrievals do. Whether they are closer to reality or not will have to be tested with co-located radiosondes or similar more accurate data, which generally do not exhibit such smooth vertical profiles as ECMWF analyses do.     

The impact of different background errors in the assimilation of satellite radiances and in-situ observational data using WRFDA for three rainfall events over Iran

Satellite radiances and in-situ observations are assimilated through Weather Research and Forecasting Data Assimilation (WRFDA) system into Advanced Research WRF (ARW) model over Iran and its neighboring area. Domain specific background error based on x and y components of wind speed (UV) control variables is calculated for WRFDA system and some sensitivity experiments are carried out to compare the impact of global background error and the domain specific background errors, both on the precipitation and 2-m temperature forecasts over Iran. Three precipitation events that occurred over the country during January, September and October 2014 are simulated in three different experiments and the results for precipitation and 2-m temperature are verified against the verifying surface observations. Results show that using domain specific background error improves 2-m temperature and 24-h accumulated precipitation forecasts consistently, while global background error may even degrade the forecasts compared to the experiments without data assimilation. The improvement in 2-m temperature is more evident during the first forecast hours and decreases significantly as the forecast length increases.     

Physical assessment of coastal vulnerability under enhanced land subsidence in Semarang,Indonesia, using multi-sensor satellite data

Research has been conducted in Semarang, Indonesia, to assess coastal vulnerability under enhanced land subsidence using multi-sensor satellite data, including the Advanced Land Observing Satellite (ALOS) Phased Array type L-band SAR (PALSAR), Landsat TM, IKONOS, and TOPEX/Poseidon. A coastal vulnerability index (CVI) was constructed to estimate the level of vulnerability of a coastline approximately 48.68?km in length using seven physical variables, namely, land subsidence, relative sea level change, coastal geomorphology, coastal slope, shoreline change, mean tidal range, and significant wave height. A comparison was also performed between a CVI calculated using seven parameters and a CVI using six parameters, the latter of which excludes the land subsidence parameter, to determine the effects of land subsidence during the coastal vulnerability assessment. This study showed that the accuracy of coastal vulnerability was increased 40% by adding the land subsidence factor (i.e., CVI 6 parameters?=?53%, CVI 7 parameters?=?93%). Moreover, Kappa coefficient indicated very good agreement (0.90) for CVI 7 parameters and fair agreement (0.3) for CVI 6 parameters. The results indicate that the area of very high vulnerability increased by 7% when land subsidence was added. Hence, using the CVI calculation including land subsidence parameters, the very high vulnerability area is determined to be 20% of the total coastline or 9.7?km of the total 48.7?km of coastline. This study proved that land subsidence has significant influence on coastal vulnerability in Semarang.     

Results of analysis of human impact on environment using the time series of vegetation satellite images around large industrial centers

The paper shows the efficiency of an application of the vegetation index image time series to determine long-term vegetation dynamics. The influence of large industrial centers of Siberia on the near-by vegetation is demonstrated. The analysis of the data shows that the influence of industrial waste is stronger in the Siberian North. These regions are characterized by critical conditions for vegetation existence. In the south of the Krasnoyarsk region, human impact is also important, but the possibility of vegetation self-rehabilitation is higher. The present-day economic situation in Russia is unique, with a temporary abrupt fall of industrial production and its following increase. Thus, we managed to analyze the degree of human impact on the environment within a relatively short-time interval.     

Lineament mapping and fractal analysis using SPOT-ASTER satellite imagery for evaluating the severity of slope weathering process

The present study describes a remote sensing approach for preparing lineament map that subsequently indicates the influence of lineament density in the severity of weathering development. In this study, SPOT-5 data, the integration of SPOT-ASTER and Digital Elevation Model (DEM) data were used and processed. The existence of an active fault system in the south of Mashhad city, NE Iran and presence of schistose rocks in this area result in the development of numerous lineament features. This region was selected for this research. Lineament features including fractures, bedding plane, cleavage, shear zones and schistosity were mapped in the study area. The results indicate that the highest concentration of lineaments occurred in the central-western and south-eastern parts of the study area, which coincide with metamorphic outcrops and NW-SE trending fault system. A comparison of lineament statistical analysis and field survey demonstrated that the structural discontinuities have a significant effect on forming and distribution of weathering profiles. It was observed that increasing the number, length and density of structural discontinuities led to strong severity in weathering, which can produce deep residual soils susceptible to landslide occurrence. The remote sensing approach developed in this study can be applicable for preparing lineament maps and evaluating the severity of weathering development in other active fault zones around the world.     

Deriving remote sensing reflectance from turbid Case II waters using green-shortwave infrared bands based model

The objectives of this study are to validate the applicability of a shortwave infrared atmospheric correction model (SWIR-based model) in deriving remote sensing reflectance in turbid Case II waters, and to improve that model using a proposed green-shortwave infrared model (GSWIR-based model). In a GSWIR-based model, the aerosol type is determined by a SWIR-based model and the reflectance due to aerosol scattering is calculated using spectral slope technology. In this study, field measurements collected from three independent cruises from two different Case II waters were used to compare models. The results indicate that both SWIR- and GSWIR-based models can be used to derive the remote sensing reflectance at visible wavelengths in turbid Case II waters, but GSWIR-based models are superior to SWIR-based models. Using the GSWIR-based model decreases uncertainty in remote sensing reflectance retrievals in turbid Case II waters by 2.6–12.1%. In addition, GSWIR-based model’s sensitivity to user-supplied parameters was determined using the numerical method, which indicated that the GSWIR-based model is more sensitive to the uncertainty of spectral slope technology than to that of aerosol type retrieval methodology. Due to much lower noise tolerance of GSWIR-based model in the blue and near-infrared regions, the GSWIR-based model performs poorly in determining remote sensing reflectance at these wavelengths, which is consistent with the GSWIR-based model’s accuracy evaluation results.     

Hypothesis-test-based landcover change detection using multi-temporal satellite images – A comparative study

Remote sensing images and technologies have been widely applied to environmental monitoring, in particular landuse/landcover classification and change detection. However, the uncertainties involved in such applications have not been fully addressed. In this paper two hypothesis-test-based change detection methods, namely the bivariate joint distribution method and the conditional distribution method, are proposed to tackle the uncertainties in change detection by making decisions based on the desired level of significance. Both methods require a data set of class-dependent no-change pixels to form the basis for class-dependent hypothesis test. Using an exemplar study area in central Taiwan, performance of the proposed methods are shown to be significantly superior to two other commonly applied methods (the post-classification comparison and the image differencing methods) in terms of the overall change detection accuracies. The conditional distribution method takes into consideration the correlation between digital numbers of the pre- and post-images and the effect of the known pre-image digital number on the range of the post-image digital number, and therefore yields the highest change detection accuracy. It is also demonstrated that the class-dependent change detection is crucial for accurate landuse/landcover change detection.     

Impact of four dimensional assimilation of satellite data on long-range simulations over the Indian region during monsoon 2006

A number of experiments were conducted to study the impact of updating model basic fields by satellite data (Quick Scatterometer (QSCAT) surface winds and Atmospheric Infrared Sounder (AIRS) temperature and humidity profiles) on long-range simulation during the Indian summer monsoon 2006. The Pennsylvania State University/National Center for Atmospheric Research (PSU/NCAR) mesoscale model version5 (MM5) and its four dimensional data assimilation (FDDA) technique was used for the numerical simulations. The spatial distribution and temporal variation in model simulated basic meteorological parameters and rainfall were verified against the observed fields from National Center for Environmental Prediction (NCEP) analysis and Tropical Rainfall Measuring Mission (TRMM), respectively. The overall analysis of the results from QSCAT surface wind assimilation as compared to control simulation (CNT; without the satellite data assimilation) suggest that a better representation of a single level wind field during model integration fail to make significant improvement in the model simulation both in the basic meteorological parameters and rainfall. The assimilation of temperature and humidity profiles from the AIRS during model integration significantly improved the rainfall prediction during monsoon period. It is found that the improvement in rainfall prediction is attributed to improved thermodynamics structure due to AIRS profile assimilation and the degree of improvement is more in temperature prediction as compared to humidity prediction. It is also found that the prediction over the regions, such as south west part of India and foothills of Himalaya, where a complex orography exists, is not significantly benefited from satellite data assimilation which highlights the need of improvement in the model in addition to a better representation of atmospheric state.     

A method in near-surface estimation of air temperature (NEAT) in times following the satellite passing time using MODIS images

Air temperature is one of the most important parameters in environmental, agricultural and water resources studies. This information is not usually always available at the required temporal and spatial resolution. The air temperature is measured at a fixed point in the meteorological stations which are dispersed and may not have the appropriate spatial resolution needed for many applications. On the other hand, MODIS satellite images have relatively acceptable spatial resolution specially for use in environmental studies. There is a methodology with which the near surface air temperature can be extracted from MODIS images at the satellite passing time with an acceptable accuracy. The goal in this study is to find a way to predict the air temperature in times after/before the satellite passing time. The procedure consists of two steps. In the first step, the relationship between the air temperature at a time in a synoptic station and the air temperature in other times up to 5 h later were modeled. In the second step, using these built up relationships, the air temperature extracted from the satellite image at the passing time was extrapolated to the next hours. Finally, the results of this extrapolation method were evaluated using the air temperatures measured at those hours and in the pixels containing some other meteorological stations. The error of the method when applied to a relatively homogeneous surface cover was about 1.5 °C. This error when applied to the next hours, was below 2 °C up to 5 h after satellite passing time. This method can be useful in some agricultural and horticultural applications in which both the spatial and temporal resolution are needed simultaneously. This product is a useful tool for frost prediction, a phenomenon that usually happens at night or early in the morning.     

Total column water vapor estimation over land using radiometer data from SAC-D/Aquarius

The aim of this study is retrieving atmospheric total column water vapor (CWV) over land surfaces using a microwave radiometer (MWR) onboard the Scientific Argentine Satellite (SAC-D/Aquarius). To research this goal, a statistical algorithm is used for the purpose of filtering the study region according to the climate type.A log-linear relationship between the brightness temperatures of the MWR and CWV obtained from Global Navigation Satellite System (GNSS) measurements was used. In this statistical algorithm, the retrieved CWV is derived from the Argentinian radiometer’s brightness temperature which works at 23.8?GHz and 36.5?GHz, and taking into account CWVs observed from GNSS stations belonging to a region sharing the same climate type. We support this idea, having found a systematic effect when applying the algorithm; it was generated for one region using the previously mentioned criteria, however, it should be applied to additional regions, especially those with other climate types.The region we analyzed is in the Southeastern United States of America, where the climate type is Cfa (Köppen - Geiger classification); this climate type includes moist subtropical mid-latitude climates, with hot, muggy summers and frequent thunderstorms. However, MWR only contains measurements taken from over ocean surfaces; therefore the determination of water vapor over land is an important contribution to extend the use of the SAC-D/Aquarius radiometer measurements beyond the ocean surface. The CWVs computed by our algorithm are compared against radiosonde CWV observations and show a bias of about ?0.6?mm, a root mean square (rms) of about 6?mm and a correlation of 0.89.     

Degradation monitoring in Silvo-pastoral systems: A case study of the Mediterranean region of Turkey

To identify policies that will promote positive effects and mitigate negative ones of grazing is a major challenge in the Silvo-pastoral system. This paper presents the role of examining land-cover change trajectories by remote sensing imagery in grazing policy monitoring. The study was conducted for Duzlercami forest ecosystem located in the Mediterranean geographical region of Turkey and administrated by the General Directorate of Forestry (GDF) of the Ministry of Forestry and Water Affairs. Time series land-cover datasets from Landsat images between 1988 and 2016 were collected and classified. To link the conversions among trajectories and grazing policy, class level landscape metrics derived from the classified images were used. To validate the approach, yearly grazing-plans managed by GDF and populations of livestock were used. Results of this research have indicated that even though there is a yearly grazing plan, overgrazing can happen on the pilot site, and it can be easily identified by the destruction of woody vegetation. The notable correlation (r²?=?0.89) between degraded woody vegetation and cattle population has occurred in the last 30?years in the landscape, and Landsat imagery can effectively support the grazing policy mapping and monitoring.     

Evaluation of GPM sampling error over major basins in Indian subcontinent using bootstrap technique

The study investigates the evaluation and comparison of sampling error for the Global Precipitation Measurement (GPM) mission orbital data products by implementing a bootstrap technique over the two major basins in the Indian subcontinent i.e the Ganga and the Mahanadi basin. The relative sampling error evaluated over both the Ganges and Mahanadi basins showed commendable results thus giving the confidence to adopt the bootstrap technique to evaluate the sampling error. The region over India with large seasonal rainfall seems to have less sampling uncertainty and vice versa with some regions showing exceptions which might be due to the difference in precipitation variability and space-time correlation length. The scale dependence was verified for four grid sizes along with seasonal time scale. Results indicate that the relative sampling error estimates are inversely proportional to the scale of the grid size. The comparative study of evaluation of sampling uncertainty to different precipitation types resulted to have maximum sampling error in GPM Microwave Imager (GMI) in comparison to Dual Precipitation Radar (DPR) convective and DPR total precipitation. Thus, the comparable results of sampling uncertainty between the major basins in the Indian sub-continent provides the user a decision making criteria before utilizing the GPM orbital products in any applications.     

Rainfall estimation over a Mediterranean region using a method based on various spectral parameters of SEVIRI-MSG

The ultimate objective of this paper is the estimation of rainfall over an area in Algeria using data from the SEVIRI radiometer (Spinning Enhanced Visible and Infrared Imager). To achieve this aim, we use a new Convective/Stratiform Rain Area Delineation Technique (CS-RADT). The satellite rainfall retrieval technique is based on various spectral parameters of SEVIRI that express microphysical and optical cloud properties. It uses a multispectral thresholding technique to distinguish between stratiform and convective clouds. This technique (CS-RADT) is applied to the complex situation of the Mediterranean climate of this region. The tests have been conducted during the rainy seasons of 2006/2007 and 2010/2011 where stratiform and convective precipitation is recorded. The developed scheme (CS-RADT) is calibrated by instantaneous meteorological radar data to determine thresholds, and then rain rates are assigned to each cloud type by using radar and rain gauge data. These calibration data are collocated with SEVIRI data in time and space.     

Improvement in the GERB short wave flux estimations over snow covered surfaces

Because space-borne radiometers do not measure the Earth’s outgoing fluxes directly, angular distribution models (ADMs) are required to relate actual radiance measurement to flux at given solar angle, satellite-viewing geometries, surface, and atmospheric conditions. The conversion of one footprint broad-band radiance into the corresponding flux requires therefore one to first characterize each footprint in terms of surface type and cloud cover properties to properly select the adequate ADM.

A snow (and sea-ice) retrieval technique based on spectral measurements from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board Meteosat 8 is presented. It has been developed to improve the scene identification and thus the ADM selection in the near-real time processing of the Geostationary Earth Radiation Budget (GERB) data at the Royal Meteorological Institute of Belgium. The improvement in the GERB short wave flux estimations over snow covered scene types resulting from angular conversion using dedicated snow ADMs (e.g., empirical snow ADMs and/or pre-computed theoretical snow ADM) instead of empirical snow-free ADMs is discussed.