Trmm 3b42 binary options


The projection gave different spatial resolution in meter distance for different products. The resizing of the resolution was done in a way such that any grid or raster cell of a particular satellite product completely coincides with the corresponding grid or raster cell of another product, that is, maintaining the same analysis extension in ArcGIS. It should be noted here that the same resolution and extension were also maintained for the interpolated gridded surface rainfall of gauge data.

After completing these background tasks, including gauge data interpolation, converting binary rainfall data to raster data followed by resizing all gridded data into a same resolution, rainfall value at the centre of each grid was extracted from all data sets observed-interpolated and satellite rainfall data.

So there were a total of 4, grid rainfall data for each day for each data set available for the verification purpose. Each satellite data set was then verified with observed-interpolated data on a daily basis followed by a summarization over month and season by means of averaging daily performance and the performance of each product was compared with one another. This is how the G-G analysis was done and the process is illustrated in Figure 3.

To determine the performance of the satellite-based rainfall estimates over the subbasin or catchment scale in the Brahmaputra Basin, catchment average daily, dekadal 10 daily accumulation , and monthly satellite-based rainfall were compared with gauge interpolated rainfall.

The ArcGIS spatial analyst tool was used to generate the average daily rainfall in each catchment from the gauge interpolated rainfall and satellite rainfall data sets as well. The analysis was based on basin average rainfall rather than the usual pixel-based comparison as elucidated by Mei et al. The conceptual and semi- distributed hydrological model relies on catchment average rainfall data; comparison of catchment average rainfall thus gives an idea of how useful the selected satellite rainfall products are in a hydrological modelling study.

The catchment average comparison between observed and satellite rainfall data has been referred to as C-C comparison in this paper. There are many methods of spatial verification available that can be used to compare rain gauge measurements with SRE.

In this study, the statistical measures used to compare the satellite estimations with the ground truth rain gauge data were taken from the results of the 3rd Algorithm Intercomparison Project of the Global Precipitation Climatology Project GPCP [ 5 , 43 — 45 ]; http: The spatial verification methods included visual verification, continuous statistics MAE, RMSE, and Mbias , and categorical statistics POD and FAR and were based on daily, dekadal 10 daily accumulation , monthly, and seasonal accumulation rain gauge and satellite estimated data.

The continuous statistics were used to evaluate the performance of the satellite products in estimating the amount of rainfall whereas categorical statistics were used to access rain detection capabilities. These categorical statistics are very much important if SRE products will be used in modelling of floods because of precipitation detection. G-G analysis was carried out using daily, monthly, and seasonal rainfall over the entire study area whereas C-C analysis was carried out using daily, dekadal, and monthly average rainfall in each catchment.

Despite being subjective in nature, simple visual comparison of mapped estimates and observations eyeball verification is one of the most effective verification methods [ 5 ]. The basinwide daily rainfall distribution of SRE and observed-interpolated rainfall map was compared visually for June 14, July 8, August 21, and September 2, The dates were chosen to test the performance of the SRE in times of heavy rainfall and correspond to the days with maximum basinwide rainfall in monsoon months in Figure 4 shows the rainfall distribution maps for July 8, the heaviest rainfall day in the heaviest monsoon, as an example.

In general, there was a good detection of rainfall distribution for most of the verified days despite some discrepancies; this can be attributed to the fact that most of high altitude areas suffer most from the rain gauge insufficiency problem [ 17 ]. Stisen and Sandholt [ 25 ] elucidate that this might be the issue of interpolation uncertainty due to low gauge density that could not properly capture rainfall pattern influenced by orography.

Another possible source of error is that all daily precipitation stations in this domain are measured at 03Z to 03Z which is not consistent with the SRE daily accumulation.

This generates a 3: Close analysis showed that all SRE rainfall patterns were consistent with the observed rainfall in as much as heavy rainfall was detected in the southwestern, central, central-south, and southeastern parts of the basin and moderate to low rainfall in the north-central and northwestern part of the basin. All the satellite rainfall maps showed a clear underestimation of daily rainfall Figure 4. One of the possible explanations of this underestimation of the SRE product is due to warm orographic rainfall that cannot be detected by microwave MW and IR sensor.

Furthermore, IR cannot solve the multiple layers of raining clouds during monsoon [ 16 ]. One of the possible reasons for this behaviour of the SRE product could be the surface snow and ice screening procedure embedded in the algorithm [ 9 ].

MW sensors largely fail to discriminate between frozen hydrometers and surface snow and ice [ 46 , 47 ]. Nevertheless, the SRE products show the distribution of the daily rainfall reasonably well.

In order to get an impression of the spatial distribution of the differences between the SRE and the interpolated rain gauge in the entire basin and not only at the gauge pixels, the different SRE were compared from the validation images on a pixel to pixel basis [ 17 ].

Under this comparison, the whole Brahmaputra was considered as a single homogenous region. The daily error found in continuous statistical analysis for the gridded rainfall was averaged over a month, and the results for the same month in the three consecutive years again were averaged to give average monthly statistics for the whole period monthly average.

The monthly average of the daily error statistics for the different satellite products from to are shown in Figure 5. The lower left panel of Figure 5 shows the monthly average correlation coefficient of all considered SRE products. The correlation coefficient is equal to or more than 0.

The correlation coefficient for other products does not exceed 0. The monthly average multiplicative bias Mbias for RFE2. This was the best result, that is, least underestimation of actual rainfall among all satellite products.

High Mbias during dry season for all satellite products is the result of very low rainfall amount detection in comparison to observed data. In this analysis, the gauge corrected TRMM 3B42 daily product did not perform well; possibly because of the very limited access to observed rain-gauge data in this region.

In summary, TRMM 3B42 is a better product when a long term average is considered, a finding that is consistent with the findings of previous studies [ 16 , 29 ]. The daily error categorical statistics for the gridded rainfall were also averaged over a month, and the results for the same month in the three consecutive years were averaged to calculate average monthly statistics for the whole period monthly average.

The monthly average of the daily error categorical statistics values for the different products is shown in Figure 6. One possible reason for the slightly better performance could be that TRMM 3B42 used gauge data compared to SRE products that used only remote sensing data. All satellite products had more or less similar results for FAR.

A direct analysis of monthly rainfall was also carried out by summing daily rainfall data to provide the monthly value, analysing the monthly error statistics, and averaging the results for the same month over the three years — The results are shown in Figure 7.

The pattern of variation of the correlation coefficient and Mbias over the year was similar for all five satellite rainfall products, although there were clear differences in overall performance.

The correlation coefficient for RFE2. In February correlation coefficient range between 0. The correlation coefficient was markedly lower during November to January for all products except RFE2. The monthly rainfall of RFE2. However, the performance of these two products during premonsoon and monsoon April to September was close, providing the same Mbias of 0.

The monsoon or rainy season is important from the agriculture point of view in terms of paddy cultivation. Figure 8 shows the spatial distribution of observed-interpolated and satellite estimated average monsoon rainfall over the period of to overall average of the monsoon season value in each of the three years.

The distribution pattern of heavy, moderate, and low rainfall areas shown by RFE2. Figure 9 shows the results of analysis with continuous statistics of the seasonal rainfall given by the different products.

Evaluating the error propagation of satellite rainfall through the prism of surface hydrology is a very challenging task because it relates too many factors, which include i specifications of the satellite rainfall products and its resolution, ii scale of the basin, iii spatiotemporal scale of the hydrologic variable of interest, iv the level of complexity and physical processes represented by the hydrologic model used, and v regional characteristics [ 28 ].

The C-C analysis aimed to evaluate the performance of satellite products in estimating the amount of rainfall in individual catchments and thus capturing the spatial variation resulting from the complex topography, significant elevation change, and scale rather than the usual pixel-based comparison [ 23 , 25 ]. These results are particularly useful for understanding the applicability of satellite rainfall for developing hydrological applications.

As the main focus was on the monsoon season and heavy rainfall that might lead to flooding, the analysis compared the performance of catchment values from SRE products compared to the observed rain gauge product on July 8, The results are shown in Table 4. There was little difference in performance among the other products.

The daily error continuous statistics for average daily rainfall in each of the catchments were averaged over a month, and the results for the same month in the three consecutive years again were averaged to give average monthly statistics for the whole period monthly average.

The monthly averages of the daily error statistics for the different satellite products from to are shown in Figure The categorical statistical method is not appropriate for C-C analysis, hence not done for this C-C analysis. The results of C-C analysis in monthly average of daily error statistics gave almost the same result as G-G analysis. On the other hand, the changes in correlation coefficient and Mbias from G-G to C-C analysis in daily rainfall analysis was not significant at all.

Also, to demonstrate the utility in flood forecasting, because depending on upstream basin size, flood routing lag time may vary from daily to dekadal or so. Visual and statistical comparisons were made of gauge-observed and satellite-based catchment average dekadal rainfall estimates catchment wide daily accumulation over the period to The results are shown in Figure There was general agreement in the overall pattern of rainfall distribution between observed and satellite estimated data, with SRE following the same trend of high and low rainfall intensity as the observed-interpolated rainfall.

However, the amount of rainfall was generally underestimated. The statistical analysis also showed that RFE2. The evolution of regional and global SRE products with high temporal and spatial resolution has opened up new opportunities for hydrological applications in data sparse regions. The main purpose of the present study was to evaluate the estimates from three global and two regional SRE products in comparison with observed rain gauge data in the Brahmaputra river basin, in order to determine their operational viability for use in hydrological applications in a region with sensitivity to orographic effects.

The evaluation was carried out at daily, dekadal, monthly, and season temporal scales for the period to using G-G and C-C approaches, with visual analysis, continuous verification statistics, and categorical verification statistics. The estimates from the five SRE products generally showed a qualitative agreement with observed rain-gauge data and rainfall events but differences in quantitative values. One possible reason for underestimation of rainfall amount is mainly attributable to warm orographic rain which cannot be detected by the IR as well as MW sensors.

IR algorithms use cloud-top temperature thresholds that are too cold for the orographic clouds; leading to underestimation of orographic rain. Passive microwave PM algorithms underestimate rainfall from orographic rain, which may not produce much ice aloft [ 47 ].

The average daily Mbias from to for RFE2. The seasonal average error statistics for RFE2. One of the possible reasons for this behaviour of SRE products could be the surface snow and ice screening procedure embedded in the algorithm due to the fact that MW sensors largely fail to discriminate between frozen hydrometeors and surface snow and ice, another possible reason for limitations in spatial and temporal sampling by the MW sensors [ 16 ].

The SRE estimates were slightly better when the river basin was divided into catchments rather than considering whole Brahmaputra as a single unit Grid-to-Grid. Time series comparison of C-C basin average dekadal rainfall from to showed strong agreement between RFE2.

The potential of RFE2. The other SRE products also performed better but still underestimated the rainfall amount. In summary, the results indicate that SRE provides reasonable rainfall estimates over the Brahmaputra river basin. Overall, in the rugged topography of the Brahmaputra river basin, SRE products which incorporated gauge data performed better than the products that only used remotely sensed data.

The effect of additional local gauges on the quality of the products was clear in the present study. It also revealed that evaluation of SRE products at monthly and seasonal temporal resolution provided better results which could be considered as useful for overall water resource assessment of the basin. The authors would like to express their sincere gratitude to their regional partners and the NOAA Climate Prediction Center for providing data for the study.

They also thank Dr. These use 'groups' a new data structure for HDF. Read a big endian binary file containing daily total precipitation at 0.

The plot contains the merged satellite precipitation and the Climate Prediction Center's morphed estimates. Read a big endian binary file containing 3-hourly precipitation at 0. The geographical extent is 60N to 60S. This creates a file half the size of those created using float values. Analogous to the previous example but it uses a big endian binary file containing 6-hourly precipitation at 0.

The geographical extent is 50N to 50S. Interpolate the 6-hourly 0. The daily precipitation estimates are obtained by merging GTS gauge observations and 3 kinds of satellite estimates: For this file, the areal average is 1. The maximum value is This is invoked by creating a string containing the desired command and, then, executing the command via the system procedure. However, many files contain values much greater than this: The script prints the min and max values for each file.

Read a HDF file containing 3-hourly precipitation at 0. The geographical extent is 40S to 40N. Unfortunately, it does not contain the geographical coordinates or temporal information. The former must be obtained via a web site while the time is in the file name. NCL can be used to read the data but it is a bit cumbersome because NCL's binary read functions only read one variable type. They lack the necessay 'granularity' to partition the different variable types.

The approach is to read the entire file as a single type and extract the appropriate information. The same record must be read for each type specification. Hence, the user must keep track of the byte counts. It unpacks the data see description in the script and creates a netCDF file for each input binary file containing the unpacked values. It also plots a simple image. The character record is included as global attributes.