Rainfall-Induced Changes in Actual Surface Backscattering Cross Sections and Effects on Rain-Rate Estimates by Spaceborne Precipitation Radar

2007 ◽  
Vol 24 (10) ◽  
pp. 1693-1709 ◽  
Author(s):  
Shinta Seto ◽  
Toshio Iguchi
Keyword(s):  
Cross Sections ◽  
Land Surface ◽  
Rain Rate ◽  
Incident Angle ◽  
Monthly Rainfall ◽  
Retrieval Algorithm ◽  
Precipitation Radar ◽  
Actual Surface ◽  
Spatial Reference ◽  
Trmm Pr

Abstract In this study, the authors used Tropical Rainfall Measuring Mission precipitation radar (TRMM PR) data to investigate changes in the actual (attenuation corrected) surface backscattering cross section (σ0e) due to changes in surface conditions induced by rainfall, the effects of changes in σ0e on the path integrated attenuation (PIA) estimates by surface reference techniques (SRTs), and the effects on rain-rate estimates by the TRMM PR standard rain-rate retrieval algorithm. Over land, σ0e is statistically higher under rainfall than under no rainfall conditions (soil moisture effect) unless the land surface is densely covered by vegetation. Over ocean, the dependence of σ0e on the incident angle differs under rainfall and no-rainfall conditions (wind speed effect). The alongtrack spatial reference (ATSR) method, one of the SRTs used in the standard algorithm, partially considers these effects, while the temporal reference (TR) method, another SRT, never involves these effects; its PIA estimates thus have negative biases over land. In the hybrid spatial reference (HSR) method used over ocean, different incident angles create different biases in PIA estimates. If the TR method is replaced by the ATSR method, the monthly rainfall amount in July 2001 all over the land within the TRMM coverage increases by 0.70%. The bias in the HSR method over ocean can be mitigated by fitting a σ0–θ curve separately to smaller incident angles and to larger incident angles. This improvement increases or decreases the monthly rainfall amounts in individual incident angle regions by up to 10%.

Rain/No-Rain Classification Methods for Microwave Radiometer Observations over Land Using Statistical Information for Brightness Temperatures under No-Rain Conditions

2005 ◽  
Vol 44 (8) ◽  
pp. 1243-1259 ◽  
Author(s):  
Shinta Seto ◽  
Nobuhiro Takahashi ◽  
Toshio Iguchi
Keyword(s):  
Brightness Temperature ◽  
Land Surface ◽  
Rain Rate ◽  
Microwave Radiometer ◽  
Tropical Rainfall Measuring Mission ◽  
Regression Equation ◽  
Retrieval Algorithm ◽  
Vertical Polarization ◽  
Surface Conditions ◽  
Background Brightness

Abstract One of the goals of the Global Precipitation Measurement project, the successor to the Tropical Rainfall Measuring Mission (TRMM), is to produce a 3-hourly global rainfall map using several spaceborne microwave radiometers. It is important, although often difficult, to classify radiometer observations over land as either “rain” or “no rain” because background land surface conditions change significantly with time and location. In this study, a no-rain brightness temperature database was created to infer land surface conditions using simultaneous observations by TRMM Microwave Imager (TMI) and precipitation radar (PR) with a resolution of 1 month and 1° latitude × 1° longitude. This paper proposes new rain/no-rain classification (RNC) methods that use the database to determine the background brightness temperature. The proposed RNC methods and the RNC method developed for the Goddard profiling algorithm (GPROF; the standard rain-rate retrieval algorithm for TMI) are applied to all TMI observations for the entire year of 2000, and the results are evaluated against the RNC made by PR as the “truth.” The first method (M1) simply uses the average brightness temperature at 85-GHz vertical polarization [denoted as TB (85 V)] under no-rain conditions as the background brightness temperature at 85-GHz vertical polarization [denoted as TBe (85 V)]. The second method (M2) uses a regression equation between TB (85 V) and TB (22 V) under no-rain conditions from the database. Here, TBe (85 V) is calculated by substituting the observed TB (22 V) into the regression equation. The ratio of accurate rain detection by GPROF to all rain occurrences detected by PR was 59%. This ratio was 57% for M1 and 63% for M2. The ratio with the weight of the rain rate was 81% for M1 and 86% for M2; it was 80% for GPROF. These comparisons were made by setting a threshold using a constant coefficient k0 to make the ratio of false rain detection to all no-rain occurrences detected by PR almost the same (approximately 0.85%) for all three methods. Further comparisons among the methods are made, and the reasons for the differences are investigated herein.

scholarly journals Evaluation of rainfall retrievals from SEVIRI reflectances over West Africa using TRMM-PR and CMORPH

2011 ◽  
Vol 15 (2) ◽  
pp. 437-451 ◽  
Author(s):  
E. L. A. Wolters ◽  
B. J. J. M. van den Hurk ◽  
R. A. Roebeling
Keyword(s):  
West Africa ◽  
Temporal Resolution ◽  
Rain Rate ◽  
Thermal Infrared ◽  
Occurrence Frequency ◽  
Added Value ◽  
Inter Tropical Convergence Zone ◽  
Trmm Pr ◽  
Spatio Temporal ◽  
Good Agreement

Abstract. This paper describes the evaluation of the KNMI Cloud Physical Properties – Precipitation Properties (CPP-PP) algorithm over West Africa. The algorithm combines condensed water path (CWP), cloud phase (CPH), cloud particle effective radius (re), and cloud-top temperature (CTT) retrievals from visible, near-infrared and thermal infrared observations of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard the Meteosat Second Generation (MSG) satellites to estimate rain occurrence frequency and rain rate. For the 2005 and 2006 monsoon seasons, it is investigated whether the CPP-PP algorithm is capable of retrieving rain occurrence frequency and rain rate over West Africa with sufficient accuracy, using Tropical Monsoon Measurement Mission Precipitation Radar (TRMM-PR) as reference. As a second goal, it is assessed whether SEVIRI is capable of monitoring the seasonal and daytime evolution of rainfall during the West African monsoon (WAM), using Climate Prediction Center Morphing Technique (CMORPH) rainfall observations. The SEVIRI-detected rainfall area agrees well with TRMM-PR, with the areal extent of rainfall by SEVIRI being ~10% larger than from TRMM-PR. The mean retrieved rain rate from CPP-PP is about 8% higher than from TRMM-PR. Examination of the TRMM-PR and CPP-PP cumulative frequency distributions revealed that differences between CPP-PP and TRMM-PR are generally within +/−10%. Relative to the AMMA rain gauge observations, CPP-PP shows very good agreement up to 5 mm h−1. However, at higher rain rates (5–16 mm h−1) CPP-PP overestimates compared to the rain gauges. With respect to the second goal of this paper, it was shown that both the accumulated precipitation and the seasonal progression of rainfall throughout the WAM is in good agreement with CMORPH, although CPP-PP retrieves higher amounts in the coastal region of West Africa. Using latitudinal Hovmüller diagrams, a fair correspondence between CPP-PP and CMORPH was found, which is reflected by high correlation coefficients (~0.7) for both rain rate and rain occurrence frequency. The daytime cycle of rainfall from CPP-PP shows distinctly different patterns for three different regions in West Africa throughout the WAM, with a decrease in dynamical range of rainfall near the Inter Tropical Convergence Zone (ITCZ). The dynamical range as retrieved from CPP-PP is larger than that from CMORPH. It is suggested that this results from both the better spatio-temporal resolution of SEVIRI, as well as from thermal infrared radiances being partly used by CMORPH, which likely smoothes the daytime precipitation signal, especially in case of cold anvils from convective systems. The promising results show that the CPP-PP algorithm, taking advantage of the high spatio-temporal resolution of SEVIRI, is of added value for monitoring daytime precipitation patterns in tropical areas.

scholarly journals Derivation and analysis of a high-resolution estimate of global permafrost zonation

2011 ◽  
Vol 5 (3) ◽  
pp. 1547-1582
Author(s):  
S. Gruber
Keyword(s):  
High Resolution ◽  
Land Surface ◽  
Permafrost Region ◽  
Large Area ◽  
Data Set ◽  
Regional Patterns ◽  
Actual Surface ◽  
Interesting Insight ◽  
Air Temperatures ◽  
Discontinuous Permafrost

Abstract. Permafrost underlies much of Earths' surface and interacts with climate, eco-systems and human systems. It is a complex phenomenon controlled by climate and (sub-) surface properties and reacts to change with variable delay. Heterogeneity and sparse data challenge the modeling of its spatial distribution. Currently, there is no data set to adequately inform global studies of permafrost. The available data set for the Northern Hemisphere is frequently used for model evaluation, but its quality and consistency are difficult to assess. A global model of permafrost extent and dataset of permafrost zonation are presented and discussed, extending earlier studies by including the Southern Hemisphere, by consistent data and methods, and most importantly, by attention to uncertainty and scaling. Established relationships between air temperature and the occurrence of permafrost are re-formulated into a model that is parametrized using published estimates. It is run with a high-resolution (<1 km) global elevation data and air temperatures based on the NCAR-NCEP reanalysis and CRU TS 2.0. The resulting data provides more spatial detail and a consistent extrapolation to remote regions, while aggregated values resemble previous studies. The estimated uncertainties affect regional patterns and aggregate number, but provide interesting insight. The permafrost area, i.e. the actual surface area underlain by permafrost, north of 60° S is estimated to be 13–18 × 106 km2 or 9–14 % of the exposed land surface. The global permafrost area including Antarctic and sub-sea permafrost is estimated to be 16–21 × 106 km2. The global permafrost region, i.e. the exposed land surface below which some permafrost can be expected, is estimated to be 22 ± 3 × 106 km2. A large proportion of this exhibits considerable topography and spatially-discontinuous permafrost, underscoring the importance of attention to scaling issues and heterogeneity in large-area models.

scholarly journals Improving GOES Advanced Baseline Imager (ABI) aerosol optical depth (AOD) retrievals using an empirical bias correction algorithm

2020 ◽  
Vol 13 (11) ◽  
pp. 5955-5975
Author(s):  
Hai Zhang ◽  
Shobha Kondragunta ◽  
Istvan Laszlo ◽  
Mi Zhou
Keyword(s):  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Bias Correction ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Infrared Imaging ◽  
Retrieval Algorithm ◽  
Correction Algorithm ◽  
Time Step

Abstract. The Advanced Baseline Imager (ABI) on board the Geostationary Operational Environmental Satellite-R (GOES-R) series enables retrieval of aerosol optical depth (AOD) from geostationary satellites using a multiband algorithm similar to those of polar-orbiting satellites' sensors, such as the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS). However, this work demonstrates that the current version of GOES-16 (GOES-East) ABI AOD has diurnally varying biases due to limitations in the land surface reflectance relationships between the 0.47 µm band and the 2.2 µm band and between the 0.64 µm band and 2.2 µm band used in the ABI AOD retrieval algorithm, which vary with the Sun–satellite geometry and NDVI (normalized difference vegetation index). To reduce these biases, an empirical bias correction algorithm has been developed based on the lowest observed ABI AOD of an adjacent 30 d period and the background AOD at each time step and at each pixel. The bias correction algorithm improves the performance of ABI AOD compared to AErosol RObotic NETwork (AERONET) AOD, especially for the high and medium (top 2) quality ABI AOD. AOD data for the period 6 August to 31 December 2018 are used to evaluate the bias correction algorithm. After bias correction, the correlation between the top 2 quality ABI AOD and AERONET AOD improves from 0.87 to 0.91, the mean bias improves from 0.04 to 0.00, and root-mean-square error (RMSE) improves from 0.09 to 0.05. These results for the bias-corrected top 2 qualities ABI AOD are comparable to those of the corrected high-quality ABI AOD. By using the top 2 qualities of ABI AOD in conjunction with the bias correction algorithm, the areal coverage of ABI AOD is increased by about 100 % without loss of data accuracy.

scholarly journals Comparison of measured brightness temperatures from SMOS with modelled ones from ORCHIDEE and H-TESSEL over the Iberian Peninsula

2015 ◽  
Vol 12 (12) ◽  
pp. 13019-13067
Author(s):  
A. Barella-Ortiz ◽  
J. Polcher ◽  
P. de Rosnay ◽  
M. Piles ◽  
E. Gelati
Keyword(s):  
Soil Moisture ◽  
Radiative Transfer ◽  
Iberian Peninsula ◽  
Land Surface ◽  
Function Analysis ◽  
Microwave Emission ◽  
Radiative Transfer Model ◽  
Retrieval Algorithm ◽  
Transfer Model ◽  
Brightness Temperatures

Abstract. L-Band radiometry is considered to be one of the most suitable techniques to estimate surface soil moisture by means of remote sensing. Brightness temperatures are key in this process, as they are the main input in the retrieval algorithm. The work exposed compares brightness temperatures measured by the Soil Moisture and Ocean Salinity (SMOS) mission to two different sets of modelled ones, over the Iberian Peninsula from 2010 to 2012. The latter were estimated using a radiative transfer model and state variables from two land surface models: (i) ORganising Carbon and Hydrology In Dynamic EcosystEms (ORCHIDEE) and (ii) Hydrology – Tiled ECMWF Scheme for Surface Exchanges over Land (H-TESSEL). The radiative transfer model used is the Community Microwave Emission Model (CMEM). A good agreement in the temporal evolution of measured and modelled brightness temperatures is observed. However, their spatial structures are not consistent between them. An Empirical Orthogonal Function analysis of the brightness temperature's error identifies a dominant structure over the South-West of the Iberian Peninsula which evolves during the year and is maximum in Fall and Winter. Hypotheses concerning forcing induced biases and assumptions made in the radiative transfer model are analysed to explain this inconsistency, but no candidate is found to be responsible for it at the moment. Further hypotheses are proposed at the end of the paper.

scholarly journals SPATIOLTEMPORAL MODELING THE IMPACT OF SURFACE CHARACTERISTICS VARIATIONS ON LAND SURFACE TEMPERATURE VARIATIONS: A CASE STUDY OF SAMALGHAN VALLY

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 401-405
Author(s):  
M. K. Firozjaei ◽  
M. Makki ◽  
J. Lentschke ◽  
M. Kiavarz ◽  
S. K. Alavipanah
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Single Channel ◽  
Incident Angle ◽  
Surface Characteristics ◽  
Slope Aspect ◽  
Biophysical Parameters ◽  
The Impact

Abstract. Spatiotemporal mapping and modeling of Land Surface Temperature (LST) variations and characterization of parameters affecting these variations are of great importance in various environmental studies. The aim of this study is a spatiotemporal modeling the impact of surface characteristics variations on LST variations for the studied area in Samalghan Valley. For this purpose, a set of satellite imagery and meteorological data measured at the synoptic station during 1988–2018, were used. First, single-channel algorithm, Tasseled Cap Transformation (TCT) and Biophysical Composition Index (BCI) were employed to estimate LST and surface biophysical parameters including brightness, greenness and wetness and BCI. Also, spatial modeling was used to modeling of terrain parameters including slope, aspect and local incident angle based on DEM. Finally, the principal component analysis (PCA) and the Partial Least Squares Regression (PLSR) were used to modeling and investigate the impact of surface characteristics variations on LST variations. The results indicated that surface characteristics vary significantly for case study in spatial and temporal dimensions. The correlation coefficient between the PC1 of LST and PC1s of brightness, greenness, wetness, BCI, DEM, and solar local incident angle were 0.65, −0.67, −0.56, 0.72, −0.43 and 0.53, respectively. Furthermore, the coefficient coefficient and RMSE between the observed LST variation and modelled LST variation based on PC1s of brightness, greenness, wetness, BCI, DEM, and local incident angle were 0.83 and 0.14, respectively. The results of study indicated the LST variation is a function of s terrain and surface biophysical parameters variations.

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