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South African Weather Service Operational Satellite Based Precipitation Estimation Technique: Applications and Improvements
Abstract and Figures
Extreme weather related to heavy or more frequent precipitation events seem to be a likely possibility for the future of our planet. While precipitation measurements can be done by means of rain gauges, the obvious disadvantages of point measurements are driving meteorologists towards remotely sensed precipitation methods. In South Africa more sophisticated and expensive nowcasting technology such as radar and lightning networks are available, supported by a fairly dense rain gauge network of about 1500 gauges. In the rest of southern Africa rainfall measurements are more difficult to obtain. The availability of the local version of the Unified Model and the Meteosat Second Generation satellite data make these products ideal components of precipitation measurement in data sparse regions such as Africa. In this article the local version of the Hydroestimator (originally from NOAA/NESDIS) is discussed as well as its applications for precipitation measurement in this region. Hourly accumulations of the Hydroestimator are currently used as a satellite based precipitation estimator for the South African Flash Flood Guidance system. However, the Hydroestimator is by no means a perfect representation of the real rainfall. In this study the Hydroestimator and the stratiform rainfall field from the Unified Model are both bias corrected and then combined into a new precipitation field which can feed into the South African Flash Flood Guidance system. This new product should provide a more accurate and comprehensive input to the Flash Flood Guidance systems in South Africa as well as southern Africa. In this way the southern African region where data is sparse and very few radars are available can have access to more accurate flash flood guidance.
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... In spatial interpolation, five measurements from rainfall stations at close proximity to the point of interest are used to estimate rainfall of that point. This near real time rainfall data is measured automatically at 550 stations evenly distributed throughout the country (de Coning and Poolman, 2010;de Coning, 2013). The minimum, maximum, as well as the computed mean and coefficient of variation of rain fall were used in this study to estimate Eucalyptus spp. ...
Forest stand volume is one of the crucial stand parameters, which influences the ability of these forests to
provide ecosystem goods and services. This study thus aimed at examining the potential of integrating
multispectral SPOT 5 image, with ancillary data (forest age and rainfall metrics) in estimating stand volume
between coppiced and planted Eucalyptus spp. in KwaZulu-Natal, South Africa. To achieve this objective,
Partial Least Squares Regression (PLSR) algorithm was used. The PLSR algorithm was implemented
by applying three tier analysis stages: stage I: using ancillary data as an independent dataset, stage II:
SPOT 5 spectral bands as an independent dataset and stage III: combined SPOT 5 spectral bands and ancillary
data. The results of the study showed that the use of an independent ancillary dataset better explained the volume of Eucalyptus spp. growing from coppices (adjusted R2 (R2Adj) = 0.54, RMSEP = 44.08 m3
/ha), when compared with those that were planted (R2Adj = 0.43, RMSEP = 53.29 m3/ha).
Similar results were also observed when SPOT 5 spectral bands were applied as an independent dataset,
whereas improved volume estimates were produced when using combined dataset. For instance, planted
Eucalyptus spp. were better predicted adjusted R2 (R2Adj) = 0.77, adjusted R2Adj = 0.59, RMSEP = 36.02 m3
/ha) when compared with those that grow from coppices (R2 = 0.76, R2Adj = 0.46, RMSEP = 40.63 m3
/ha). Overall, the findings of this study demonstrated the relevance of multi-source data in ecosystems odelling.
... The corrected 3B42V7-based simulations performed slightly better than that using the gauge-based precipitation, implying that the 3B42V7 product can potentially improve the discharge simulations over the data-sparse Chindwin River. In regions where a sufficiently dense gauge network is unavailable, satellite-derived rainfall can be a critical data source for identifying hazards from small-scale rainfall and flood events [52]. streamflow simulations were achieved via bias correction. ...
Satellite precipitation products from the Global Precipitation Measurement (GPM) mission and its predecessor the Tropical Rainfall Measuring Mission (TRMM) are a critical data source for hydrological applications in ungauged basins. This study conducted an initial and early evaluation of the performance of the Integrated Multi-satellite Retrievals for GPM (IMERG) final run and the TRMM Multi-satellite Precipitation Analysis 3B42V7 precipitation products, and their feasibility in streamflow simulations in the Chindwin River basin, Myanmar, from April 2014 to December 2015 was also assessed. Results show that, although IMERG and 3B42V7 can potentially capture the spatiotemporal patterns of historical precipitation, the two products contain considerable errors. Compared with 3B42V7, no significant improvements were found in IMERG. Moreover, 3B42V7 outperformed IMERG at daily and monthly scales and in heavy rain detections at four out of five gauges. The large errors in IMERG and 3B42V7 distinctly propagated to streamflow simulations via the Xinanjiang hydrological model, with a significant underestimation of total runoffand high flows. The bias correction of the satellite precipitation effectively improved the streamflow simulations. The 3B42V7-based streamflow simulations performed better than the gauge-based simulations. In general, IMERG and 3B42V7 are feasible for use in streamflow simulations in the study area, although 3B42V7 is better suited than IMERG.
A number of studies have thus far explored the utility of fine multispectral; hyperspectral and active imaging sensors for determining stand volume.
In Africa and other developing countries these data types have gained more popularity for project-based applications, rather than for “wall to wall” applications
Free and readily available multispectral sensors thus remains crucial in estimating stand
Additionally, these data types do not necessarily require complex pre-processing and analysis when compared to its counterparts.
Accurate, reliable, and up-to-date forest stand volume information is a prerequisite for a detailed evaluation of commercial forest resources and their sustainable management. Commercial forest responses to global climate change remain uncertain, and hence the mapping of stand volume as carbon sinks is fundamentally important in understanding the role of forests in stabilizing climate change effects. The aim of this study was to examine the utility of stochastic gradient boosting (SGB) and multi-source data to predict stand volume of a Eucalyptus plantation in South Africa. The SGB ensemble, random forest (RF), and stepwise multiple-linear regression (SMLR) were used to predict Eucalyptus stand volume and other related tree-structural attributes such as mean tree height and mean diameter at breast height (DBH). Multi-source data consisted of SPOT-5 raw spectral features (four bands), 14 spectral vegetation indices, rainfall data, and stand age. When all variables were used, the SGB algorithm showed that stand volume can be accurately estimated (R2 = 0.78 and RMSE = 33.16 m3 ha−1 (23.01% of the mean)). The competing RF ensemble produced an R2 value of 0.76 and a RMSE value of 37.28 m3 ha−1 (38.28% of the mean). SMLR on the other hand, produced an R2 value of 0.65 and an RMSE value of 42.50 m3 ha−1 (42.50% of the mean). Our study further showed that Eucalyptus mean tree height (R2 = 0.83 and RMSE = 1.63 m (9.08% of the mean)) and mean diameter at breast height (R2 = 0.74 and RMSE = 1.06 (7.89% of the mean)) can also be reasonably predicted using SGB and multi-source data. Furthermore, when the most important SGB model-selected variables were used for prediction, the predictive accuracies improved significantly for mean DBH (R2 = 0.81 and RMSE = 1.21 cm (6.12% of the mean)), mean tree height (R2 = 0.86 and RMSE = 1.39 m (7.02% of the mean)), and stand volume (R2 = 0.83 and RMSE = 29.58 m3 ha−1 (17.63% of the mean)). These results underscore the importance of integrating multi-source data with remotely sensed data for predicting Eucalyptus stand volume and related tree-structural attributes.
Flash floods are among the most devastating natural weather hazards in the United States, causing an average of more than 225 deaths and $4 billion in property damage annually. As a result, prediction of flash floods in an accurate and timely fashion is one of the most important challenges in weather prediction. Data from geostationary and polar-orbiting satellites are significant sources of information for the diagnosis and prediction of heavy precipitation and flash floods. Geostationary satellites are especially important for their unique ability simultaneously to observe the atmosphere and its cloud cover from the global scale down to the storm scale at high resolution in both time (every 15 min) and space (1-4 km). This capability makes geostationary satellite data ideally suited for estimating and predicting heavy precipitation, especially during flash-flood events. Pre- sented in this paper are current and future efforts in the National Environmental Satellite, Data, and Information Service that support National Weather Service River Forecast Centers and Weather Forecast Offices during extreme-precipitation events.
This paper presents a description, sensitivity analyses, sample results, validation, and the recent progress done on the development of a new satellite rainfall estimation technique in the National Environmental Satellite Data and Information Service (NESDIS) at the National Oceanic and Atmospheric Administration (NOAA). The technique, called the auto-estimator, runs in real time for applications to flash flood forecasting, numerical modeling, and operational hydrology. The auto-estimator uses the Geoestationary Operational Environmental Satellite-8 and -9 in the infrared (IR) 10.7-mm band to compute real-time precipitation amounts based on a power-law regression algorithm. This regression is derived from a statistical analysis between surface radar-derived instantaneous rainfall estimates and satellite-derived IR cloud-top temperatures collocated in time and space. The rainfall rate estimates are adjusted for different moisture regimes using the most recent fields of precipitable water and relative humidity generated by the National Centers for Environmental Prediction Eta Model. In addition, a mask is computed to restrict rain to regions satisfying two criteria: (a) the growth rate of the cloud as a function of the temperature change of the cloud tops in two consecutive IR images must be positive, and (b) the spatial gradients of the cloud-top temperature field must show distinct and isolated cold cores in the cloud-top surface. Both the growth rate and the gradient corrections are useful for locating heavy precipitation cores. The auto-estimator has been used experimentally for almost 3 yr to provide real-time instantaneous rainfall rate estimates, average hourly estimates, and 3-, 6-, and 24-h accumulations over the conterminous 48 United States and nearby ocean areas. The NOAA/NESDIS Satellite Analyses Branch (SAB) has examined the accuracy of the rainfall estimates daily for a variety of storm systems. They have determined that the algorithm produces useful 1-6-h estimates for flash flood monitoring but exaggerates the area of precipitation causing overestimation of 24-h rainfall total associated with slow-moving, cold-topped mesoscale convective systems. The SAB analyses have also shown a tendency for underestimation of rainfall rates in warm-top stratiform cloud systems. Until further improvements, the use of this technique for stratiform events should be considered with caution. The authors validate the hourly rainfall rates of the auto-estimator using gauge-adjusted radar precipitation products (with radar bias removed) in three distinct cases. Results show that the auto-estimator has modest skill at 1-h time resolution for a spatial resolution of 12 km. Results improve with larger grid sizes (48 by 48 km or larger).
This research presents a step by step description of the procedure to correct high resolution satellite rainfall estimation for parallax error and orographic effects, and shows how these adjustments can be performed on any satellite rainfall estimation technique. The procedure is tested on the experimental automatic rainfall estimation technique currently in use by the National Environment Satellite Data, and Information Service (NESDIS), at the National and Oceanic Administration (NOAA)
The first satellite of the Meteosat Second Generation series (MSG-1) was launched on 29 August 2002 by an Ariane 5 rocket. The commissioning of SEVIRI started on 27 November 2002, and the first SEVIRI image was taken at 12:30z on 28 November. MSG-1 commissioning will continue until the end of 2003. During the commissioning phase, dedicated SEVIRI instrument tests have been conducted to verify the instrument functionality and performances. This paper summarises the main results of the SEVIRI imaging functionality and performance tests executed during commissioning. These SEVIRI commissioning results have mainly been obtained from the Image Quality Ground Support Equipment (IQGSE), however, comparable results are expected from the image processing system that will be used for routine operations (IMPF).
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