Mitigation of saturation in satellite pasture measurement via incorporation of a statistical pasture growth model

Measurement of pasture biomass is useful to farmers, as it enables timely and accurate management decisions. Satellite pasture measurement allows this information to be obtained with minimal time and labour on the part of the farmer. However, the accuracy of satellite measurements for high levels of pasture biomass can be impacted by a phenomenon called saturation, in which the response of the satellite estimate to increased biomass is diminished in situations of high biomass. In this investigation, a statistical pasture growth model was combined with satellite pasture measurements, with the aim of mitigating the effect of saturation on estimation accuracy. Data were captured for five farms, across two regions and an 18–21 month measurement period. Where satellite measurements appeared to be saturated, the growth model estimate was substituted. This process resulted in improved accuracy (R2 improved from 0.672 to 0.703; RMSE improved from 334 to 309 kg DM/ha; and average bias improved from -62 to -9 kg DM/ha). The statistical improvements were more pronounced where terrestrial estimates were higher so the impact of saturation would be greatest. These results indicate that the problem of saturation in satellite pasture measurement can be addressed by the incorporation of modelled data. Prior research has predicted that improved accuracy of pasture measurement would be associated with increased profitability, and this work helps achieve that goal for farmers using satellite measurement services.

CMEMS Primary production from satellite remote sensing: spatial and temporal evolution and comparison with other products

<p>Half of the global primary production is produced in the ocean by phytoplankton and the reaction of photosynthesis. For the marine environment, primary production is at the basis for the food web, by the supply of energy for higher trophic levels. Monitor primary production appears therefore to be a guideline to reach sustainable fisheries. In addition to its role on the trophic web, primary production is also important for its role on CO<sub>2</sub> fluxes. Indeed, while phytoplankton creates matter from nutrients and CO<sub>2</sub>. The produced matter can be grazed by higher trophic levels or sink towards sediment. Amount of carbon sequestrated and exported out of the productive layer give some clues efficiencies of the oceanic biological carbon pump. Primary production is therefore important not only for economic resources, but also for climatic studies, to investigate if the ocean is a carbon sink or sources.</p><p>A strategy of algorithm validation / inter-comparison was used as part as the CMEMS project to identify most accurate primary production algorithm among the most used in the literature.</p><p>Primary production validation is based on the commonly used comparison with in situ data, as well as the frequency and the intensity of the annual bloom in different basin. Inter-comparison with model were performed at the basin scale of the Mediterranean Sea to assess the robustness and the consistency of different type of estimates.</p><p>Satellite estimate of primary production, as proposed by CMEMS, give now access to an archive of 21 years for user community, to investigate evolution of primary production at the global scale or in specific basin.</p><p> </p>

Evaluation of High-Resolution Multisatellite and Reanalysis Rainfall Products over East Africa

The performance of six satellite-based and three newly released reanalysis rainfall estimates are evaluated at daily time scale and spatial grid size of 0.25 degrees during the period of 2000 to 2013 over the Upper Blue Nile Basin, Ethiopia, with the view of improving the reliability of precipitation estimates of the wet (June to September) and secondary rainy (March to May) seasons. The study evaluated both adjusted and unadjusted satellite-based products of TMPA, CMORPH, PERSIANN, and ECMWF ERA-Interim reanalysis as well as Multi-Source Weighted-Ensemble Precipitation (MSWEP) estimates. Among the six satellite-based rainfall products, adjusted CMORPH exhibits the best accuracy of the wet season rainfall estimate. In the secondary rainy season, unadjusted CMORPH and 3B42V7 are nearly equivalent in terms of bias, POD, and CSI error metrics. All error metric statistics show that MSWEP outperform both unadjusted and gauge adjusted ERA-Interim estimates. The magnitude of error metrics is linearly increasing with increasing percentile threshold values of gauge rainfall categories. Overall, all precipitation datasets need further improvement in terms of detection during the occurrence of high rainfall intensity. MSWEP detects higher percentiles values better than satellite estimate in the wet and poor in the secondary rainy seasons.

Ensemble Data Assimilation Applied to RAINEX Observations of Hurricane Katrina (2005)

An ensemble Kalman filter (EnKF) based on the Weather Research and Forecasting model is applied to generate ensemble analyses and forecasts of Hurricane Katrina (2005) and the surrounding area every 6 h over the lifetime of the storm on a nested domain. Analyses are derived from assimilating conventional in situ observations, reconnaissance dropsondes, including data taken during the Hurricane Rainband and Intensity Exchange Experiment (RAINEX), and tropical cyclone position estimates. Observation assimilation at individual times consistently reduces errors in tropical cyclone position, but not necessarily in intensity; however, withholding observations leads to significantly larger errors in both quantities. Analysis increments for observations near the tropical cyclone are dominated by changes in vortex position, and these increments increase the asymmetric structure of the storm. Data denial experiments indicate that dropsondes deployed in the synoptic environment provide minimal benefit to the outer domain; however, dropsondes deployed within the tropical cyclone lead to significant reductions in position and intensity errors on the inner domain. Specifically, errors in the inner domain ensemble-mean 6-h forecasts of minimum pressure are 70% larger when dropsonde data is not assimilated. Precipitation fields are qualitatively similar to Tropical Rainfall Measuring Mission (TRMM) satellite estimates, although model values are double the values of the satellite estimate. Moreover, the spinup period and initial imbalance in EnKF-initialized WRF forecasts is less than starting the model from a GFS analysis. Ensemble-mean 48-h forecasts initialized with EnKF analyses have track and intensity errors that are 50% smaller than GFS and NHC official forecasts.