Spatio-temporal variability of daily precipitation concentration in Spain based on a high-resolution gridded data set

Abstract. The high spatio-temporal variability of precipitation is often difficult to characterise due to limited measurements. The available low-resolution global reanalysis datasets inadequately represent the spatio-temporal variability of precipitation relevant to catchment hydrology. The Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia (BARRA) provides a high-resolution atmospheric reanalysis dataset across the Australasian region. For hydrometeorological applications, however, it is essential to properly evaluate the sub-daily precipitation from this reanalysis. In this regard, this paper evaluates the sub-daily precipitation from BARRA for a period of 6 years (2010–2015) over Australia against point observations and blended radar products. We utilise a range of existing and bespoke metrics for evaluation at point and spatial scales. We examine bias in quantile estimates and spatial displacement of sub-daily rainfall at a point scale. At a spatial scale, we use the fractions skill score as a spatial evaluation metric. The results show that the performance of BARRA precipitation depends on spatial location, with poorer performance in tropical relative to temperate regions. A possible spatial displacement during large rainfall is also found at point locations. This displacement, evaluated by comparing the distribution of rainfall within a day, could be quantified by considering the neighbourhood grids. On spatial evaluation, hourly precipitation from BARRA is found to be skilful at a spatial scale of less than 100 km (150 km) for a threshold of 75th percentile (90th percentile) at most of the locations. The performance across all the metrics improves significantly at time resolutions higher than 3 h. Our evaluations illustrate that the BARRA precipitation, despite discernible spatial displacements, serves as a useful dataset for Australia, especially at sub-daily resolutions. Users of BARRA are recommended to properly account for possible spatio-temporal displacement errors, especially for applications where the spatial and temporal characteristics of rainfall are deemed very important.

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Ability of an Australian reanalysis dataset to characterise sub-daily precipitation

10.5194/hess-2019-432 ◽

2019 ◽

Author(s):

Suwash Chandra Acharya ◽

Rory Nathan ◽

Quan J. Wang ◽

Chun-Hsu Su ◽

Nathan Eizenberg

Keyword(s):

High Resolution ◽

Spatial Scale ◽

Temporal Variability ◽

Daily Precipitation ◽

Spatial Scales ◽

Daily Rainfall ◽

Spatial Displacement ◽

Reanalysis Dataset ◽

Spatio Temporal ◽

Spatial Evaluation

Abstract. The high spatio-temporal variability of precipitation is often difficult to characterise due to limited measurements. The available low-resolution global reanalysis datasets inadequately represent the spatio-temporal variability of precipitation relevant to catchment hydrology. The Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia (BARRA) provides a high-resolution atmospheric reanalysis dataset across the Australasian region. For hydrometeorological applications, however, it is essential to properly evaluate the sub-daily precipitation from this reanalysis. In this regard, this paper evaluates the sub-daily precipitation from BARRA for a period of 6 years (2010–2015) over Australia against point observations and blended radar products. We utilise a range of existing and bespoke metrics for evaluation at point and spatial scales. We examine bias in quantile estimates and spatial displacement of sub-daily rainfall at a point scale. At a spatial scale, we use the Fractions Skill Score as a spatial evaluation metric. The results show that the performance of BARRA precipitation depends on spatial location with poorer performance in tropical relative to temperate regions. A possible spatial displacement during large rainfall is also found at point locations. This displacement, evaluated by comparing the distribution of rainfall within a day, could be quantified by considering the neighbourhood grids. On spatial evaluation, hourly precipitation from BARRA are found to be skilful at a spatial scale of less than 100 km (150 km) for a threshold of 75 % quantile (90 % quantile) at most of the locations. The performance across all the metrics improves significantly at time resolutions higher than 3 h. Our evaluations illustrate that the BARRA precipitation, despite discernible spatial displacements, serves as a useful dataset for Australia, especially at sub-daily resolutions. Users of BARRA are recommended to properly account for possible spatio-temporal displacement errors, especially for applications where the spatial and temporal characteristics of rainfall are deemed very important.

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Analysis of precipitation extremes based on satellite and high-resolution gridded data set over Mediterranean basin

Atmospheric Research ◽

10.1016/j.atmosres.2013.04.009 ◽

2013 ◽

Vol 131 ◽

pp. 46-59 ◽

Cited By ~ 48

Author(s):

P.T. Nastos ◽

J. Kapsomenakis ◽

K.C. Douvis

Keyword(s):

High Resolution ◽

Mediterranean Basin ◽

Precipitation Extremes ◽

Gridded Data ◽

Data Set

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Comparing the Köppen-Geiger, Feddema, and UNEP Climate Classifications: An Application to Iran

10.21203/rs.3.rs-508056/v1 ◽

2021 ◽

Author(s):

Tayeb Raziei

Keyword(s):

High Resolution ◽

Satisfactory Agreement ◽

Pairwise Comparison ◽

Combined Effect ◽

Sea Surface ◽

Gridded Data ◽

Northern Iran ◽

Data Set ◽

Climate Maps ◽

Semi Arid

Abstract This study introduces the climates of Iran defined by Köppen-Geiger, Feddema’s, and UNPEP classifications that applied to a high-resolution ground-based gridded data set relative to the 1985–2017 period. Ten Köppen-Geiger climate types were found for Iran, from which Bwh, Bsk, Csa, Bsh, and Bwk cumulatively account for more than 98% of the territory. Likewise, from 36 possible Feddema’s climate types, Iran possesses fifteen climate types from which the Dry Cool, Semiarid Torrid, Semiarid Hot, Semiarid Warm, Dry warm, Semiarid Cool, and Moist Cool climates collectively occupied approximately 93% of the country. Similarly, arid, semi-arid, humid, and sub-humid UNEP climate types characterized more than 98% of Iran. A few other vertically stratified climates appeared at the highlands of Iran just because of changes in elevation and slope aspects of the mountains. The combined effect of topography and vicinity to sea also creates very distinct climate types in northern Iran. The climate maps of the three used methods reflect the joint effects of topography, latitudinal variation, and land/sea surface contrast on the climate of Iran. A pairwise comparison made between the three classifications showed a satisfactory agreement between the three schemes in representing the main climate types of Iran.

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High Resolution Spatio-Temporal Variability of Heat Wave Impacts Quantified by Thermal Comfort Indices

10.21203/rs.3.rs-839624/v1 ◽

2021 ◽

Author(s):

C NEETHU ◽

KV Ramesh

Keyword(s):

High Resolution ◽

Thermal Comfort ◽

Temporal Variability ◽

Heat Waves ◽

Wrf Model ◽

Apparent Temperature ◽

Average Intensity ◽

Human Thermal Comfort ◽

Spatio Temporal ◽

Comfort Indices

Abstract Heat waves are increasing in frequency and also exhibit high spatial variability in its distribution over India. There are limited studies focused on the weather related human thermal comfort over India due to non-availability of high resolution (HR) climate data. Here we develop dynamically downscaled HR (4x4 km) daily climate information for the months of April to June during 2001-2016 using a regional climate model called Weather Research and Forecasting (WRF) Model, which are validated with station observations. The thermal comfort and its spatio-temporal variability over India are quantified in terms of indices like Excessive Heat Factor (EHF), Heat Index (HI), Humidex, Apparent Temperature (AT) and Wet Bulb Globe Temperature (WBGT). The daily surface air temperature and thermal comfort indices of HR WRF model simulations are in good agreement with station observations. The results show that there is an increasing trend in annual heat waves coverage (22240km2/year), annual frequency (0.07 days/year) and average intensity (0.04 °C/year) during 2001-2016. The distributions of indices have spatial and temporal variability. The days with severe discomfort are significantly increasing (99% significance level) over north India and it is quantified with increase of extreme category of indices at the rate of EHF (15.9%), HI (14.9%), Humidex (15.9%), AT (13.4%) and WBGT (13.8%). During heat waves, prolonged exposure or physical activity under sun will led to adverse health impacts and it is mostly observed over northwest and south eastern states. These findings stress the need for developing suitable mitigation strategies for a sustainable ecosystem

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