Abstract
Tanzania has inadequate weather stations (28-synoptic weather stations), which are sparsely distributed over complex topographic terrain. Many places, especially rural areas, have no stations to monitor weather and climate. In this study, we evaluate the performance of ENACT-MAPROOM products over Tanzania with the aim of assessing their potential to supplement observed weather and climate data, especially over areas where there is limited number of weather stations. Monthly rainfall total and monthly averaged minimum and maximum temperatures from ENACT-MAPROOM are evaluated against observed data from 23 weather stations. The evaluation is limited to analyze how well the ENACT-MAPROOM products reproduce climatological trends, annual cycles and inter-annual variability of rainfall, minimum and maximum temperatures. Statistical analysis recommended by the World Meteorological Organization (WMO) that includes that correlation and trend analysis are used. It is found that ENACT-MAPROOM products reproduce the climatological trends, annual cycles and inter-annual variability of rainfall, minimum and maximum temperatures over most stations. The statistical relationship between ENACT-MAPROOM products against observed data from 23 weather stations using Pearson correlation coefficient indicates that ENACT-MAPROOM products bear strong and statistically significant correlation coefficient to observed data. The overall evaluation here finds high skills of ENACT-MAPROOM products in representing rainfall and temperature over Tanzania, suggesting their potential use in planning and decision making especially over areas with limited number of weather stations.
Highlights
Climate change and variability depends on long-term observational climate datasets [1]
We evaluate the performance of ENACT-MAPROOM products over Tanzania with the aim of assessing their potential to supplement observed weather and climate data, especially over areas where there is limited number of weather stations
This figure was produced by averaging the values of observed rainfall total from 15 stations in the bimodal area Figure 4(a) and 8 stations in the unimodal area Figure 4(b), to get single representative time series for each rainfall pattern, that are compared with rainfall from MAP ROOM products after interpolated to the location of weather stations
Summary
Climate change and variability depends on long-term observational climate datasets [1]. These datasets, especially in developing countries are observed by ground based weather stations. The number of ground based weather stations in developing countries are not adequate to provide climate dataset at high temporal and spatial resolution [2] [3]. Tanzania has few weather stations (28-synoptic weather stations) which are sparsely distributed over complex topographical terrain [4]. In order to overcome some of the challenges of data from ground based weather stations (large data gaps, sparse stations networks), satellite based gridded climate data are generated at high temporal and spatial resolutions
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