Meteorological data is useful for varied applications and sectors ranging from weather and climate forecasting, landscape planning to disaster management among others. However, the availability of these data requires ...Meteorological data is useful for varied applications and sectors ranging from weather and climate forecasting, landscape planning to disaster management among others. However, the availability of these data requires a good network of manual meteorological stations and other support systems for its collection, recording, processing, archiving, communication and dissemination. In sub-Saharan Africa, such networks are limited due to low investment and capacity. To bridge this gap, the National Meteorological Services in Kenya and few others from African countries have moved to install a number of Automatic Weather Stations (AWSs) in the past decade including a few additions from private institutions and individuals. Although these AWSs have the potential to improve the existing observation network and the early warning systems in the region, the quality and capacity of the data collected from the stations are not well exploited. This is mainly due to low confidence, by data users, in electronically observed data. In this study, we set out to confirm that electronically observed data is of comparable quality to a human observer recorded data, and can thus be used to bridge data gaps at temporal and spatial scales. To assess this potential, we applied the simple Pearson correlation method and other statistical tests and approaches by conducting inter-comparison analysis of weather observations from the manual synoptic station and data from two Automatic Weather Stations (TAHMO and 3D-PAWS) co-located at KMD Headquarters to establish existing consistencies and variances in several weather parameters. Results show there is comparable consistency in most of the weather parameters between the three stations. Strong associations were noted between the TAHMO and manual station data for minimum (r = 0.65) and maximum temperatures (r = 0.86) and the maximum temperature between TAHMO and 3DPAWS (r = 0.56). Similar associations were indicated for surface pressure (r = 0.99) and RH (r > 0.6) with the weakest correlations occurring in wind direction and speed. The Shapiro test for normality assumption indicated that the distribution of several parameters compared between the 3 stations were normally distributed (p > 0.05). We conclude that these findings can be used as a basis for wider use of data sets from Automatic Weather Stations in Kenya and elsewhere. This can inform various applications in weather and climate related decisions.展开更多
Atmospheric reanalysis data are an important data source for studying weather and climate systems.The sea surface wind and sea level pressure observations measured from a real-time buoy system deployed in Kenya’s off...Atmospheric reanalysis data are an important data source for studying weather and climate systems.The sea surface wind and sea level pressure observations measured from a real-time buoy system deployed in Kenya’s offshore area in 2019 conducted jointly by Chinese and Kenyan scientists were used to evaluate the performance of the major high-frequency atmospheric reanalysis products in the western Indian Ocean region.Compared with observations,the sea level pressure field could be accurately simulated using the atmospheric reanalysis data.However,significant discrepancies existed between the surface wind reanalysis data,especially between meridional wind and the observational data.Most of the data provide a complete understanding of sea level pressure,except for the Japanese 55-year Reanalysis data,which hold a significant system bias.The Modern-Era Reanalysis for Research and Applications,Version-2,provides an improved description of all datasets.All the reanalysis datasets for zonal wind underestimate the strength during the study period.Among reanalysis data,NCEP-DOE Atmospheric Model Intercomparison Project reanalysis data presents an inaccurate description due to the worst correlation with the observations.For meridional wind,most reanalysis datasets underestimate the variance,while the European Centre for Medium-Range Weather Forecasts Atmospheric Composition Reanalysis 4 has a larger variance than the observations.In addition to the original data comparison,the diurnal variability of sea level pressure and surface wind are also assessed,and the result indicates that the diurnal variations have a significant gap between observation and reanalysis data.This study indicates that the current high-frequency reanalysis data still have disadvantages when describing the atmospheric parameters in the Western Indian Ocean region.展开更多
文摘Meteorological data is useful for varied applications and sectors ranging from weather and climate forecasting, landscape planning to disaster management among others. However, the availability of these data requires a good network of manual meteorological stations and other support systems for its collection, recording, processing, archiving, communication and dissemination. In sub-Saharan Africa, such networks are limited due to low investment and capacity. To bridge this gap, the National Meteorological Services in Kenya and few others from African countries have moved to install a number of Automatic Weather Stations (AWSs) in the past decade including a few additions from private institutions and individuals. Although these AWSs have the potential to improve the existing observation network and the early warning systems in the region, the quality and capacity of the data collected from the stations are not well exploited. This is mainly due to low confidence, by data users, in electronically observed data. In this study, we set out to confirm that electronically observed data is of comparable quality to a human observer recorded data, and can thus be used to bridge data gaps at temporal and spatial scales. To assess this potential, we applied the simple Pearson correlation method and other statistical tests and approaches by conducting inter-comparison analysis of weather observations from the manual synoptic station and data from two Automatic Weather Stations (TAHMO and 3D-PAWS) co-located at KMD Headquarters to establish existing consistencies and variances in several weather parameters. Results show there is comparable consistency in most of the weather parameters between the three stations. Strong associations were noted between the TAHMO and manual station data for minimum (r = 0.65) and maximum temperatures (r = 0.86) and the maximum temperature between TAHMO and 3DPAWS (r = 0.56). Similar associations were indicated for surface pressure (r = 0.99) and RH (r > 0.6) with the weakest correlations occurring in wind direction and speed. The Shapiro test for normality assumption indicated that the distribution of several parameters compared between the 3 stations were normally distributed (p > 0.05). We conclude that these findings can be used as a basis for wider use of data sets from Automatic Weather Stations in Kenya and elsewhere. This can inform various applications in weather and climate related decisions.
基金supported by the Global Change and Air-Sea Interaction Program(No.GASI-04-QYQH-03)the Taishan Scholars Program of Shandong Province(No.tsqn 201909165)+3 种基金the National Natural Science Foundation of China(No.41876028)the Global Change and Air-Sea Interaction Program(No.GASI-01-WIND-STwin)the Shandong Science and Technology Foundation(No.2013GRC 31504)the Marine S&T Fund of Shandong Province for Pilot National Laboratory for Marine Science and Technology(Qingdao)(No.2022QNLM010103-3).
文摘Atmospheric reanalysis data are an important data source for studying weather and climate systems.The sea surface wind and sea level pressure observations measured from a real-time buoy system deployed in Kenya’s offshore area in 2019 conducted jointly by Chinese and Kenyan scientists were used to evaluate the performance of the major high-frequency atmospheric reanalysis products in the western Indian Ocean region.Compared with observations,the sea level pressure field could be accurately simulated using the atmospheric reanalysis data.However,significant discrepancies existed between the surface wind reanalysis data,especially between meridional wind and the observational data.Most of the data provide a complete understanding of sea level pressure,except for the Japanese 55-year Reanalysis data,which hold a significant system bias.The Modern-Era Reanalysis for Research and Applications,Version-2,provides an improved description of all datasets.All the reanalysis datasets for zonal wind underestimate the strength during the study period.Among reanalysis data,NCEP-DOE Atmospheric Model Intercomparison Project reanalysis data presents an inaccurate description due to the worst correlation with the observations.For meridional wind,most reanalysis datasets underestimate the variance,while the European Centre for Medium-Range Weather Forecasts Atmospheric Composition Reanalysis 4 has a larger variance than the observations.In addition to the original data comparison,the diurnal variability of sea level pressure and surface wind are also assessed,and the result indicates that the diurnal variations have a significant gap between observation and reanalysis data.This study indicates that the current high-frequency reanalysis data still have disadvantages when describing the atmospheric parameters in the Western Indian Ocean region.
