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

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

Hydrology and Earth System Sciences ◽

10.5194/hess-24-2951-2020 ◽

2020 ◽

Vol 24 (6) ◽

pp. 2951-2962

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

International Journal of Climatology ◽

10.1002/joc.5387 ◽

2017 ◽

Vol 38 ◽

pp. e518-e530 ◽

Cited By ~ 25

Author(s):

R. Serrano-Notivoli ◽

J. Martín-Vide ◽

M. A. Saz ◽

L. A. Longares ◽

S. Beguería ◽

...

Keyword(s):

High Resolution ◽

Temporal Variability ◽

Daily Precipitation ◽

Gridded Data ◽

Data Set ◽

Precipitation Concentration ◽

Spatio Temporal

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Airborne observation during KORUS-AQ show aerosol optical depth are more spatially self-consistent than aerosol intensive properties

10.5194/acp-2021-1012 ◽

2022 ◽

Author(s):

Samuel E. LeBlanc ◽

Michal Segal-Rozenhaimer ◽

Jens Redemann ◽

Connor J. Flynn ◽

Roy R. Johnson ◽

...

Keyword(s):

Air Quality ◽

Aerosol Optical Depth ◽

Optical Depth ◽

Temporal Variability ◽

Spatial Scales ◽

Aerosol Emission ◽

Fine Mode ◽

Predominant Factor ◽

Spatio Temporal ◽

Microphysical Processes

Abstract. Aerosol particles can be emitted, transported, removed, or transformed, leading to aerosol variability at scales impacting the climate (days to years and over hundreds of kilometers) or the air quality (hours to days and from meters to hundreds of kilometers). We present the temporal and spatial scales of changes in AOD (Aerosol Optical Depth), and aerosol size (using Angstrom Exponent; AE, and Fine-Mode-Fraction; FMF) over Korea during the 2016 KORUS-AQ (KORea-US Air Quality) atmospheric experiment. We use measurements and retrievals of aerosol optical properties from airborne instruments for remote sensing (4STAR; Spectrometers for Sky-Scanning Sun Tracking Atmospheric Research) and in situ (LARGE; NASA Langley Aerosol Research Group Experiment) on board the NASA DC-8, geostationary satellite (GOCI; Geostationary Ocean Color Imager; Yonsei aerosol retrieval (YAER) version 2) and reanalysis (MERRA-2; Modern-Era Retrospective Analysis for Research and Applications, version 2). Measurements from 4STAR when flying below 500 m, show an average AOD at 501 nm of 0.43 and an average AE of 1.15 with large standard deviation (0.32 and 0.26 for AOD and AE respectively) likely due to mixing of different aerosol types (fine and coarse mode). The majority of AODs due to fine mode aerosol is observed at altitudes lower than 2 km. Even though there are large variations, for 18 out of the 20 flight days, the column AOD measurements by 4STAR along the NASA DC-8 flight trajectories matches the south-Korean regional average derived from GOCI. We also observed that, contrary to prevalent understanding, AE and FMF are more spatially variable than AOD during KORUS-AQ, even when accounting for potential sampling biases by using Monte Carlo resampling. Averaging between measurements and model for the entire KORUS-AQ period, a reduction in correlation by 15 % is 65.0 km for AOD and shorter at 22.7 km for AE. While there are observational and model differences, the predominant factor influencing spatial-temporal homogeneity is the meteorological period. High spatio-temporal variability occur during the dynamic period (25–31 May), and low spatio-temporal variability occur during blocking Rex pattern (01–07 June). The changes in spatial variability scales between AOD and FMF/AE, while inter-related, indicate that microphysical processes that impact mostly the dominant aerosol size, like aerosol particle formation, growth, and coagulation, vary at shorter scales than the aerosol concentration processes that mostly impact AOD, like aerosol emission, transport, and removal.

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Evaluation of Asian summer precipitation in different configurations of a high-resolution general circulation model in a range of decision-relevant spatial scales

Hydrology and Earth System Sciences ◽

10.5194/hess-25-6381-2021 ◽

2021 ◽

Vol 25 (12) ◽

pp. 6381-6405

Author(s):

Mark R. Muetzelfeldt ◽

Reinhard Schiemann ◽

Andrew G. Turner ◽

Nicholas P. Klingaman ◽

Pier Luigi Vidale ◽

...

Keyword(s):

High Resolution ◽

Spatial Scale ◽

Diurnal Cycle ◽

General Circulation ◽

Spatial Scales ◽

Summer Precipitation ◽

Physical Geography ◽

Simulated Precipitation ◽

The Mean ◽

Precipitation Bias

Abstract. High-resolution general circulation models (GCMs) can provide new insights into the simulated distribution of global precipitation. We evaluate how summer precipitation is represented over Asia in global simulations with a grid length of 14 km. Three simulations were performed: one with a convection parametrization, one with convection represented explicitly by the model's dynamics, and a hybrid simulation with only shallow and mid-level convection parametrized. We evaluate the mean simulated precipitation and the diurnal cycle of the amount, frequency, and intensity of the precipitation against satellite observations of precipitation from the Climate Prediction Center morphing method (CMORPH). We also compare the high-resolution simulations with coarser simulations that use parametrized convection. The simulated and observed precipitation is averaged over spatial scales defined by the hydrological catchment basins; these provide a natural spatial scale for performing decision-relevant analysis that is tied to the underlying regional physical geography. By selecting basins of different sizes, we evaluate the simulations as a function of the spatial scale. A new BAsin-Scale Model Assessment ToolkIt (BASMATI) is described, which facilitates this analysis. We find that there are strong wet biases (locally up to 72 mm d−1 at small spatial scales) in the mean precipitation over mountainous regions such as the Himalayas. The explicit convection simulation worsens existing wet and dry biases compared to the parametrized convection simulation. When the analysis is performed at different basin scales, the precipitation bias decreases as the spatial scales increase for all the simulations; the lowest-resolution simulation has the smallest root mean squared error compared to CMORPH. In the simulations, a positive mean precipitation bias over China is primarily found to be due to too frequent precipitation for the parametrized convection simulation and too intense precipitation for the explicit convection simulation. The simulated diurnal cycle of precipitation is strongly affected by the representation of convection: parametrized convection produces a peak in precipitation too close to midday over land, whereas explicit convection produces a peak that is closer to the late afternoon peak seen in observations. At increasing spatial scale, the representation of the diurnal cycle in the explicit and hybrid convection simulations improves when compared to CMORPH; this is not true for any of the parametrized simulations. Some of the strengths and weaknesses of simulated precipitation in a high-resolution GCM are found: the diurnal cycle is improved at all spatial scales with convection parametrization disabled, the interaction of the flow with orography exacerbates existing biases for mean precipitation in the high-resolution simulations, and parametrized simulations produce similar diurnal cycles regardless of their resolution. The need for tuning the high-resolution simulations is made clear. Our approach for evaluating simulated precipitation across a range of scales is widely applicable to other GCMs.

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seNorge2 daily precipitation, an observational gridded dataset over Norway from 1957 to the present day

Earth System Science Data ◽

10.5194/essd-10-235-2018 ◽

2018 ◽

Vol 10 (1) ◽

pp. 235-249 ◽

Cited By ~ 37

Author(s):

Cristian Lussana ◽

Tuomo Saloranta ◽

Thomas Skaugen ◽

Jan Magnusson ◽

Ole Einar Tveito ◽

...

Keyword(s):

High Resolution ◽

Daily Precipitation ◽

Spatial Scales ◽

National Level ◽

Calibration Procedure ◽

Geographical Information ◽

Optimal Interpolation ◽

Small Scale ◽

Meteorological Forcing ◽

Atmospheric Sciences

Abstract. The conventional climate gridded datasets based on observations only are widely used in atmospheric sciences; our focus in this paper is on climate and hydrology. On the Norwegian mainland, seNorge2 provides high-resolution fields of daily total precipitation for applications requiring long-term datasets at regional or national level, where the challenge is to simulate small-scale processes often taking place in complex terrain. The dataset constitutes a valuable meteorological input for snow and hydrological simulations; it is updated daily and presented on a high-resolution grid (1 km of grid spacing). The climate archive goes back to 1957. The spatial interpolation scheme builds upon classical methods, such as optimal interpolation and successive-correction schemes. An original approach based on (spatial) scale-separation concepts has been implemented which uses geographical coordinates and elevation as complementary information in the interpolation. seNorge2 daily precipitation fields represent local precipitation features at spatial scales of a few kilometers, depending on the station network density. In the surroundings of a station or in dense station areas, the predictions are quite accurate even for intense precipitation. For most of the grid points, the performances are comparable to or better than a state-of-the-art pan-European dataset (E-OBS), because of the higher effective resolution of seNorge2. However, in very data-sparse areas, such as in the mountainous region of southern Norway, seNorge2 underestimates precipitation because it does not make use of enough geographical information to compensate for the lack of observations. The evaluation of seNorge2 as the meteorological forcing for the seNorge snow model and the DDD (Distance Distribution Dynamics) rainfall–runoff model shows that both models have been able to make profitable use of seNorge2, partly because of the automatic calibration procedure they incorporate for precipitation. The seNorge2 dataset 1957–2015 is available at https://doi.org/10.5281/zenodo.845733. Daily updates from 2015 onwards are available at http://thredds.met.no/thredds/catalog/metusers/senorge2/seNorge2/provisional_archive/PREC1d/gridded_dataset/catalog.html.

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Less rain and rainy days - lessons from 45 years of rainfall data (1971 to 2015) in the Kathmandu Valley, Nepal

10.21203/rs.3.rs-167685/v1 ◽

2021 ◽

Author(s):

Rajaram Prajapati ◽

Rocky Talchabhadel ◽

Priya Silwal ◽

Surabhi Upadhyay ◽

Brandon Ertis ◽

...

Keyword(s):

Temporal Variability ◽

Temporal Trends ◽

Daily Rainfall ◽

Rainfall Data ◽

Annual Rainfall ◽

Kathmandu Valley ◽

Light Rain ◽

Mk Test ◽

Spatio Temporal ◽

Rainy Days

Abstract Understanding spatio-temporal variability in rainfall patterns is crucial for evaluating water balances needed for water resources planning and management. This paper investigates spatio-temporal variability in rainfall and assesses the frequency of daily rainfall observations from seven stations in the Kathmandu Valley, Nepal, from 1971–2015. Daily rainfall totals were classified into five classes, namely, A (light rain, daily rainfall < 10 mm in a day), B (between 10–50 mm), C (between 50–100 mm), D (between 100–150 mm) and E (> 150 mm). The relationship between daily rainfall and rainfall frequency of various rainfall rate classes were analysed. Kriging method was used for interpolation in interpreting seasonal and annual rainfall data and spatial maps were generated using QGIS. The Mann-Kendall (MK) test was performed to determine the temporal trends and Theil-Sen’s (TS) slope estimator was used in quantifying the magnitude of trends. Mountain stations showed a decreasing trend in rainfall for all seasons, ranging from − 8.4 mm/year at Sankhu to -21.8 mm/year at Thankot, whereas, a mixed pattern was found on the Valley floor. Mean annual rainfall in the Valley was 1610 mm. Both annual rainfall and the number of rainy days decreased in the Kathmandu Valley over the study period. The study indicated a significant reduction in rainfall after 2000. Since springs and shallow groundwater are the primary sources of water supply for residents in the Kathmandu Valley, it is apparent that decreasing rainfall will have (and is already having) an adverse impact on domestic, industrial, and agricultural water supplies, and the livelihoods of people.

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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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A bayesian hierarchical model to predict spatio-temporal variability in river water quality at 102 catchments

10.5194/egusphere-egu2020-4725 ◽

2020 ◽

Author(s):

Danlu Guo ◽

Anna Lintern ◽

Angus Webb ◽

Dongryeol Ryu ◽

Ulrike Bende-Michl ◽

...

Keyword(s):

Water Quality ◽

Statistical Model ◽

Temporal Variability ◽

Stream Water ◽

Spatial Scales ◽

Water Quality Monitoring ◽

Catchment Management ◽

Stream Water Quality ◽

Bayesian Hierarchical ◽

Spatio Temporal

<div> <div> <div> <div>Our current capacity to model stream water quality is limited particularly at large spatial scales across multiple catchments. To address this, we developed a Bayesian hierarchical statistical model to simulate the spatio-temporal variability in stream water quality across the state of Victoria, Australia. The model was developed using monthly water quality monitoring data over 21 years, across 102 catchments, which span over 130,000 km<sup>2</sup>. The modelling focused on six key water quality constituents: total suspended solids (TSS), total phosphorus (TP), filterable reactive phosphorus (FRP), total Kjeldahl nitrogen (TKN), nitrate-nitrite (NO<sub>x</sub>), and electrical conductivity (EC). The model structure was informed by knowledge of the key factors driving water quality variation, which had been identified in two preceding studies using the same dataset. Apart from FRP, which is hardly explainable (19.9%), the model explains 38.2% (NO<sub>x</sub>) to 88.6% (EC) of total spatio-temporal variability in water quality. Across constituents, the model generally captures over half of the observed spatial variability; temporal variability remains largely unexplained across all catchments, while long-term trends are well captured. The model is best used to predict proportional changes in water quality in a Box-Cox transformed scale, but can have substantial bias if used to predict absolute values for high concentrations. This model can assist catchment management by (1) identifying hot-spots and hot moments for waterway pollution; (2) predicting effects of catchment changes on water quality e.g. urbanization or forestation; and (3) identifying and explaining major water quality trends and changes. Further model improvements should focus on: (1) alternative statistical model structures to improve fitting for truncated data, for constituents where a large amount of data below the detection-limit; and (2) better representation of non-conservative constituents (e.g. FRP) by accounting for important biogeochemical processes.</div> </div> </div> </div>

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An evaluation of daily precipitation from atmospheric reanalyses over Australia

10.5194/hess-2018-607 ◽

2019 ◽

Cited By ~ 1

Author(s):

Suwash Chandra Acharya ◽

Rory Nathan ◽

Quan J. Wang ◽

Chun-Hsu Su ◽

Nathan Eizenberg

Keyword(s):

Daily Precipitation ◽

Transition Probabilities ◽

Reanalysis Dataset ◽

Atmospheric Processes ◽

Precipitation Estimates ◽

Spatio Temporal ◽

Regional Reanalysis ◽

Global Reanalysis ◽

Better Than

Abstract. An accurate representation of spatio-temporal characteristics of precipitation fields is fundamental for many hydro-meteorological analyses but is often limited by the paucity of gauges. Reanalysis models provide systematic methods of representing atmospheric processes to produce datasets of spatio-temporal precipitation estimates. The precipitation from the reanalysis datasets should, however, be evaluated thoroughly before use because it is inferred from physical parameterization. In this paper, we evaluated the precipitation dataset from the Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia (BARRA) and compared it against (a) gauged point observations, (b) an interpolated gridded dataset based on gauged point observations (AWAP), and (c) a global reanalysis dataset (ERA-Interim). We utilized a range of evaluation metrics such as continuous metrics (correlation, bias, variability, modified Kling-Gupta efficiency), categorical metrics, and other statistics (wet day frequency, transition probabilities and quantiles) to ascertain the quality of the dataset. BARRA, in comparison with ERA-Interim, shows a better representation of rainfall of larger magnitude at both point and grid scale of 5 km. BARRA also consistently reproduces the distribution of wet days and transition probabilities. The performance of BARRA varies spatially, with better performance in the temperate zone than in the arid and tropical zones. A point-to-grid evaluation based on correlation, bias and modified Kling-Gupta efficiency (KGE') indicates that ERA-Interim performs on par or better than BARRA. However, on a spatial scale, BARRA outperforms AWAP in terms of KGE' score and the components of the KGE' score. Our evaluation illustrates that BARRA, with richer spatial variations in climatology of daily precipitation, provides an improved representation of precipitation compared with the coarser ERA-Interim. It is a useful complement to existing precipitation datasets for Australia, especially in sparsely gauged regions.

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