High-resolution Regional Reanalysis Research Articles

Occurrence and trends of historical tropical cyclone rainfall on near-coastal regions of Australia

Extreme rainfall driven by tropical cyclones (TCs) has profound effects on Australian coastlines at both local and regional scales. Here, we develop methods for comparing TC-driven widespread and localised rainfall on three broad coastal regions of tropical Australia (west, north and east). Trends, average recurrence intervals (ARIs) and the fractional contribution of TC rainfall are explored in three historical datasets: Australian Gridded Climate Data (AGCD), ECMWF Reanalysis (ver. 5, ERA5) and the Bureau of Meteorology Atmospheric high resolution Regional Reanalysis for Australia (ver. 1, BARRA1). Results for trends and ARIs between the different datasets are generally inconsistent and also differ between regions, partially owing to the short-term temporal records of some of the data as well as inconsistencies in extreme values between datasets. By contrast, there is a general agreement between all datasets on the fractional contribution of TC rainfall, signalling an increase in recent years. This result is considered together with the trend towards fewer TCs occurring in this region over recent decades, indicating a trend towards increased rainfall intensity per TC on average, assuming steady landfall rates. The methods developed here can be applied easily to other data types such as regional climate model experiments, facilitating a multiple lines of evidence approach that incorporates both observational-based and model-based data. This research is intended to help provide new methods and guidance for identifying trends in TC-driven extreme rainfall, relevant for enhanced planning and adaptation to the impacts of these extreme weather systems.

Comparison of a novel machine learning approach with dynamical downscaling for Australian precipitation

Dynamical downscaling (DD), and machine learning (ML) based techniques have been widely applied to downscale global climate models and reanalyses to a finer spatiotemporal scale, but the relative performance of these two methods remains unclear. We implement an ML regression approach using a multi-layer perceptron (MLP) with a novel loss function to downscale coarse-resolution precipitation from the Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia from grids of 12–48 km to 5 km, using the Australia Gridded Climate Data observations as the target. A separate MLP is developed for each coarse grid to predict the fine grid values within it, by combining coarse-scale time-varying meteorological variables with fine-scale static surface properties as predictors. The resulting predictions (on out-of-sample test periods) are more accurate than DD in capturing the rainfall climatology, as well as the frequency distribution and spatiotemporal variability of daily precipitation, reducing biases in daily extremes by 15%–85% with 12 km prediction fields. When prediction fields are coarsened, the skill of the MLP decreases—at 24 km relative bias increases by ∼10%, and at 48 km it increases by another ∼4%—but skill remains comparable to or, for some metrics, much better than DD. These results show that ML-based downscaling benefits from higher-resolution driving data but can still improve on DD (and at far less computational cost) when downscaling from a global climate model grid of ∼50 km.

Diurnal MCSs Precede the Genesis of Tropical Cyclone Mora (2017): The Role of Convectively Forced Gravity Waves

Abstract A novel high-resolution regional reanalysis is used to investigate the mesoscale processes that preceded the formation of Tropical Cyclone (TC) Mora (2017). Both satellite observations and the regional reanalysis show early morning mesoscale convective systems (MCSs) persistently initiated and organized in the downshear quadrant of the preexisting tropical disturbance a few days prior to the genesis of TC Mora. The diurnal MCSs gradually enhanced the meso-α-scale vortex near the center of the preexisting tropical disturbance through vortex stretching, providing a vorticity-rich and moist environment for the following burst of deep convection and enhancement of the meso-β-scale vortex. The regional reanalysis shows that the gravity waves that radiated from afternoon convection over the northern coast of the Bay of Bengal might play an important role in modulating the diurnal cycle of pregenesis MCSs. The diurnal convectively forced gravity waves increased the tropospheric stability, reduced the column saturation fraction, and suppressed deep convection within the preexisting tropical disturbance from noon to evening. A similar quasi-diurnal cycle of organized deep convection prior to TC genesis has also been observed over other basins. However, modeling studies are needed to conclusively demonstrate the relationships between the gravity waves and pregenesis diurnal MCSs. Also, whether diurnal gravity waves play a similar role in modulating the pregenesis deep convection in other TCs is worth future investigations. Significance Statement Tropical cyclogenesis is a process by which a less organized weather system in the tropics develops into a tropical cyclone (TC). Observations indicate that thunderstorms occurring prior to the tropical cyclogenesis often show a distinct quasi-diurnal cycle, while the related physical mechanisms are still unclear. In this study, we used a novel high-resolution dataset to investigate the diurnal thunderstorms occurring prior to the genesis of TC Mora (2017). We find that the pregenesis diurnal thunderstorms played a crucial role in spinning up the circulation of the atmosphere and provided a favorable environment for the rapid formation of Mora. It is likely that gravity waves emitted by afternoon thunderstorms over the inland region were responsible for regulating the diurnal variation of pregenesis thunderstorms over the ocean.

Multi-Mission Satellite Detection and Tracking of October 2019 Sabiti Oil Spill in the Red Sea

A multi-mission satellite remote sensing (MSRS) approach is explored to detect and track leaked oil from the Sabiti oil tanker accident that occurred in the central Red Sea on 11 October 2019 (RSOS-2019). The spilled oil spread rapidly and reached the coastal land near Jeddah, the second largest city of KSA, on 17 October. Different oil spill detection algorithms were implemented on SAR and optical sensor-based satellite images to track the oil spill. Sentinel-1 SAR images were most efficient at detecting the spread and thickness of RSOS-2019, but their spatio-temporal coverage greatly limits their use for tracking the oil movement. The spread and propagation of oil were well captured by Sentinel-2 images up to three weeks after the accident day, in agreement with the SAR images. MODIS successfully detected the narrow patch of oil that was leaked on the incident day and the widespread oil patches two days after. Landsat-8 RGB composite and thermal infrared images captured the oil spill on 13 October. By filtering clouds from the Meteosat images through sequential analysis, the spread and movement of the oil patches were efficiently tracked on 13 October. PlanetScope images available between 12 and 17 October enabled tracking of the oil near the coastal waters. The inferred oil spill movements are consistent with the ocean currents as revealed by a high-resolution regional ocean reanalysis. Our results demonstrate the potential of the MSRS approach to detect and track oil spills in the open and coastal waters of the Red Sea in near real-time.

Analysis of extreme wind gusts using a high-resolution Australian Regional Reanalysis

The characterisation of extreme wind gust speeds has historically relied on data from observations, while numerically modelling these events is limited due to their rarity and localised nature. However, the ongoing increase in computational power now allows extreme events to be characterised in fine-scale numerical data. Extreme gust events are examined using 24 years of 1.5 km horizontal grid reanalysis data for eastern Australia, and compared to those based on 1-minute data from station observations. We estimate return values over long periods using the generalised Pareto distribution (GPD) peaks-over-threshold approach. An automated algorithm that selects an optimal threshold at each grid point is outlined and gives similar results to an approach based on pre-fixing the shape factor value similar to what is applied in Australian building design standards. We also present a decision tree model that skillfully distinguishes between convective (i.e., thunderstorm) and synoptic (e.g., frontal system and cyclone) gust events using only hourly reanalysis data. The reanalysis shows high synoptic gust intensities and frequencies along the coast, consistent with station observations, and that synoptic-type extreme gusts are enhanced by topography (higher occurrence frequencies in mountain areas) while convective-type events are less affected. The reanalysis systematically overestimates parameterised gust activity, while compensating for this at many locations by underestimating the resolved wind speed, thus often producing gusts close to observed. With the proviso that these issues are considered, we find that the information on wind gusts provided by reanalysis data may be quite useful especially in regions with low observational network coverage.

Evaluating Winter Precipitation over the Western Himalayas in a High-Resolution Indian Regional Reanalysis Using Multisource Climate Datasets

Abstract Considerable uncertainties are associated with precipitation characteristics over the western Himalayan region (WHR). These are due to typically small-scale but high-intensity storms caused by the complex topography that are under-resolved by a sparse gauge network. Additionally, both satellite and gauge precipitation measurements remain subject to systematic errors, typically resulting in underestimation over mountainous terrains. Reanalysis datasets provide prospective alternative but are limited by their resolution, which has so far been too coarse to properly resolve orographic precipitation. In this study, we evaluate and cross compare Indian Monsoon Data Assimilation and Analysis (IMDAA), the first high-resolution (12 km) regional reanalysis over India, with various precipitation products during winter season over WHR. We demonstrate IMDAA’s efficiency in representing winter precipitation characteristics at seasonal, diurnal, interannual scales, as well as heavy precipitation associated with western disturbances (WDs). IMDAA shows closer agreement to other reanalyses than to gauge-based and satellite products in error and bias analysis. Although depicting higher magnitudes, its fine resolution allows a much closer insight into localized spatial patterns and the diurnal cycle, a key advantage over other datasets. Mean winter precipitation over WHR shows a significant decreasing trend in IMDAA, despite no significant trend in the frequency of WDs tracked in either IMDAA or ERA5. The study also exhibits the potential use of IMDAA for characterizing winter atmospheric dynamics, both for climatological studies and during WD activity such as localized valley winds. Overall, these findings highlight the potential utility for IMDAA in conducting monitoring and climate change impact assessment studies over the fragile western Himalayan ecosystem.

The impact of humidity on Australia’s operational heatwave services

More frequent and intense heatwaves in the last decade have challenged humanitarian, health and meteorological authorities to mitigate impact. Meteorological heatwave monitoring and prediction services vary between heatwave definitions which either include humidity or are based only on temperature. Incorporation of humidity into human health heatwave studies and warning services has been variable. Whilst higher humidity is a known stressor during heatwaves, humidity is known to confound interpretation of heatwave data and can be difficult to monitor and forecast.This study examines the effect of humidity on diagnosed heatwave severity across Australia’s diverse climate zones. Dry bulb temperature is used as the only input into the Bureau of Meteorology’s current operational Excess Heat Factor (EHF) index. Alternative humidity-affected temperature indices (Apparent Temperature, Wet Bulb Globe Temperature and Heat Index) are examined for suitability as input to EHF to compare the incidence of dry and humidity-affected heatwave severity within Australia. This paper uses maximum and minimum dry and humidity affected temperature indices extracted from Australia’s Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia (BARRA).Our investigation demonstrates Australia’s operational temperature-only percentile-based heatwave severity service provides effective heatwave warning guidance for five of Australia’s six diverse climate zones. However, rare very dry or very humid heatwaves in the tropics require both dry bulb temperature-only and Heat Index versions of Excess Heat Factor (EHF) severity index to provide competent operational heatwave early warning guidance.

The MONARCH high-resolution reanalysis of desert dust aerosol over Northern Africa, the Middle East and Europe (2007–2016)

Abstract. One of the challenges in studying desert dust aerosol along with its numerous interactions and impacts is the paucity of direct in situ measurements, particularly in the areas most affected by dust storms. Satellites typically provide column-integrated aerosol measurements, but observationally constrained continuous 3D dust fields are needed to assess dust variability, climate effects and impacts upon a variety of socio-economic sectors. Here, we present a high-resolution regional reanalysis data set of desert dust aerosols that covers Northern Africa, the Middle East and Europe along with the Mediterranean Sea and parts of central Asia and the Atlantic and Indian oceans between 2007 and 2016. The horizontal resolution is 0.1∘ latitude × 0.1∘ longitude in a rotated grid, and the temporal resolution is 3 h. The reanalysis was produced using local ensemble transform Kalman filter (LETKF) data assimilation in the Multiscale Online Nonhydrostatic AtmospheRe CHemistry model (MONARCH) developed at the Barcelona Supercomputing Center (BSC). The assimilated data are coarse-mode dust optical depth retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue Level 2 products. The reanalysis data set consists of upper-air variables (dust mass concentrations and the extinction coefficient), surface variables (dust deposition and solar irradiance fields among them) and total column variables (e.g. dust optical depth and load). Some dust variables, such as concentrations and wet and dry deposition, are expressed for a binned size distribution that ranges from 0.2 to 20 µm in particle diameter. Both analysis and first-guess (analysis-initialized simulation) fields are available for the variables that are diagnosed from the state vector. A set of ensemble statistics is archived for each output variable, namely the ensemble mean, standard deviation, maximum and median. The spatial and temporal distribution of the dust fields follows well-known dust cycle features controlled by seasonal changes in meteorology and vegetation cover. The analysis is statistically closer to the assimilated retrievals than the first guess, which proves the consistency of the data assimilation method. Independent evaluation using Aerosol Robotic Network (AERONET) dust-filtered optical depth retrievals indicates that the reanalysis data set is highly accurate (mean bias = −0.05, RMSE = 0.12 and r = 0.81 when compared to retrievals from the spectral de-convolution algorithm on a 3-hourly basis). Verification statistics are broadly homogeneous in space and time with regional differences that can be partly attributed to model limitations (e.g. poor representation of small-scale emission processes), the presence of aerosols other than dust in the observations used in the evaluation and differences in the number of observations among seasons. Such a reliable high-resolution historical record of atmospheric desert dust will allow a better quantification of dust impacts upon key sectors of society and economy, including health, solar energy production and transportation. The reanalysis data set (Di Tomaso et al., 2021) is distributed via Thematic Real-time Environmental Distributed Data Services (THREDDS) at BSC and is freely available at http://hdl.handle.net/21.12146/c6d4a608-5de3-47f6-a004-67cb1d498d98 (last access: 10 June 2022).

Development of East Asia Regional Reanalysis based on advanced hybrid gain data assimilation method and evaluation with E3DVAR, ERA-5, and ERA-Interim reanalysis

Abstract. The East Asia Regional Reanalysis (EARR) system is developed based on the advanced hybrid gain data assimilation method (AdvHG) using the Weather Research and Forecasting (WRF) model and conventional observations. Based on EARR, the high-resolution regional reanalysis and reforecast fields are produced with 12 km horizontal resolution over East Asia for 2010–2019. The newly proposed AdvHG is based on the hybrid gain approach, weighting two different analyses for an optimal analysis. The AdvHG differs from the hybrid gain in that (1) E3DVAR is used instead of EnKF, (2) 6 h forecast of ERA5 is used to be more consistent with WRF, and (3) the preexisting, state-of-the-art reanalysis is used. Thus, the AdvHG can be regarded as an efficient approach for generating regional reanalysis datasets thanks to cost savings as well as the use of the state-of-the-art reanalysis. The upper-air variables of EARR are verified with those of ERA5 for January and July 2017 and the 10-year period 2010–2019. For upper-air variables, ERA5 outperforms EARR over 2 years, whereas EARR outperforms (shows comparable performance to) ERA-I and E3DVAR for January 2017 (July 2017). EARR represents precipitation better than ERA5 for January and July 2017. Therefore, although the uncertainties of upper-air variables of EARR need to be considered when analyzing them, the precipitation of EARR is more accurate than that of ERA5 for both seasons. The EARR data presented here can be downloaded from https://doi.org/10.7910/DVN/7P8MZT (Yang and Kim, 2021b) for data on pressure levels and https://doi.org/10.7910/DVN/Q07VRC (Yang and Kim, 2021c) for precipitation.

Temporal disaggregation of daily rainfall measurements using regional reanalysis for hydrological applications

Accurate rainfall datasets with high temporal and spatial resolutions are crucial for most hydrological applications. One potentially valuable source of rainfall data that has consistent spatial and temporal resolutions are atmospheric reanalysis products. However, while such data sets can provide a physically consistent representation of rainfall over large spatial and temporal extents, they are generally less accurate than observed datasets at daily scales. In contrast, while the gauge measurements are accurate source of rainfall data, sub-daily observations are spatially sparse and are of shorter length than daily observations. While the temporal resolution of daily observations can be enhanced using temporal disaggregation methods, they are often applied stochastically with a focus on capturing the fine-scale statistical properties rather than generating a best estimate time series useful for hindcasting purposes. The increasing availability of high resolution regional reanalysis products prompts the question whether they can be used to temporally disaggregate daily observations to derive high-resolution estimates of sub-daily rainfalls suitable for hydrologic applications. This study investigates the efficacy of a simple disaggregation approach to temporally disaggregate daily rainfalls to hourly values using a regional reanalysis at moderate spatial resolutions. The approach is tested on attributes relevant to a wide range of hydrological applications. The selected performance metrics include the distribution and frequency of various sub-daily rainfall accumulations, statistics characterising the sequencing and central tendency of sub-daily rainfalls, and the efficacy of areal estimates of sub-daily rainfalls for simulating catchment streamflows. Categorical evaluation shows that the disaggregated rainfalls reduce the frequency of false alarms and improves the probability of detection compared to the use of raw reanalysis estimates. However, a mixed performance in capturing fine-scale statistical characteristics suggests that the disaggregation approach is less robust for applications that rely solely on high-resolution rainfall characteristics. In hydrological evaluation, when compared to estimates based on raw reanalysis or uniformly disaggregated daily observations, the disaggregated catchment rainfalls improve the simulation of the magnitude and timing of peak flows, and the accuracy of derived flood frequency estimates. The proposed disaggregation method is easily applied to any high-resolution dataset and has the potential to be used in hydrological applications that rely heavily on sub-daily characterisation, with a varying performance across the target applications.

Remote Sensing and Meteorological Data Fusion in Predicting Bushfire Severity: A Case Study from Victoria, Australia

The extent and severity of bushfires in a landscape are largely governed by meteorological conditions. An accurate understanding of the interactions of meteorological variables and fire behaviour in the landscape is very complex, yet possible. In exploring such understanding, we used 2693 high-confidence active fire points recorded by a Moderate Resolution Imaging Spectroradiometer (MODIS) sensor for nine different bushfires that occurred in Victoria between 1 January 2009 and 31 March 2009. These fires include the Black Saturday Bushfires of 7 February 2009, one of the worst bushfires in Australian history. For each fire point, 62 different meteorological parameters of bushfire time were extracted from Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia (BARRA) data. These remote sensing and meteorological datasets were fused and further processed in assessing their relative importance using four different tree-based ensemble machine learning models, namely, Random Forest (RF), Fuzzy Forest (FF), Boosted Regression Tree (BRT), and Extreme Gradient Boosting (XGBoost). Google Earth Engine (GEE) and Landsat images were used in deriving the response variable–Relative Difference Normalised Burn Ratio (RdNBR), which was selected by comparing its performance against Difference Normalised Burn Ratio (dNBR). Our findings demonstrate that the FF algorithm utilising the Weighted Gene Coexpression Network Analysis (WGCNA) method has the best predictive performance of 96.50%, assessed against 10-fold cross-validation. The result shows that the relative influence of the variables on bushfire severity is in the following order: (1) soil moisture, (2) soil temperature, (3) air pressure, (4) air temperature, (5) vertical wind, and (6) relative humidity. This highlights the importance of soil meteorology in bushfire severity analysis, often excluded in bushfire severity research. Further, this study provides a scientific basis for choosing a subset of meteorological variables for bushfire severity prediction depending on their relative importance. The optimal subset of high-ranked variables is extremely useful in constructing simplified and computationally efficient surrogate models, which can be particularly useful for the rapid assessment of bushfire severity for operational bushfire management and effective mitigation efforts.

Monsoon precipitation characteristics and extreme precipitation events over Northwest India using Indian high resolution regional reanalysis

Northwest India, known as the breadbasket of the country, is extremely vulnerable to ramifications of extreme rainfall events (EREs) such as flash floods, landslides, agricultural and infrastructural damages. Nevertheless, the characteristics of EREs are less explored over Northwest India compared to other parts of the country. Hence, this study investigates the spatiotemporal variability of EREs using India's first highest resolution regional atmospheric reanalysis, Indian Monsoon Data Assimilation and Analysis (IMDAA) during Indian Summer Monsoon (ISM) for the period 1979–2018. Prior to understanding the EREs, we evaluated IMDAA's ability to represent general ISM characteristics using reanalysis and observations. Our analysis reveals that IMDAA realistically represents the ISM salient features along with a more accurate spatial distribution of summer precipitation compared to India Meteorological Department (IMD) observations, albeit with some overestimations. The mean ISM precipitation over Northwest India is found to be increasing significantly at 95% confidence level. Furthermore, EREs from IMD and TRMM exhibit increasing trends over Northwest India, which also conforms with IMDAA results (one event every two years). The rising (declining) trend in convective available potential energy (convective inhibition energy) signifies that the atmosphere over Northwest India is becoming more unstable during ISM. This enables it to hold more moisture, which is transported to the region from Bay of Bengal through cyclonic vorticity over Central India. Finally, the inter-parameter correlation analysis reveals that IMDAA reanalysis has succeeded in better representation of the physical linkages among moisture availability, instability, and convective precipitation formation over Northwest India compared to the global reanalysis products such as ERA5.

BARRA v1.0: kilometre-scale downscaling of an Australian regional atmospheric reanalysis over four midlatitude domains

Abstract. Regional reanalyses provide a dynamically consistent recreation of past weather observations at scales useful for local-scale environmental applications. The development of convection-permitting models (CPMs) in numerical weather prediction has facilitated the creation of kilometre-scale (1–4 km) regional reanalysis and climate projections. The Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia (BARRA) also aims to realize the benefits of these high-resolution models over Australian sub-regions for applications such as fire danger research by nesting them in BARRA's 12 km regional reanalysis (BARRA-R). Four midlatitude sub-regions are centred on Perth in Western Australia, Adelaide in South Australia, Sydney in New South Wales (NSW), and Tasmania. The resulting 29-year 1.5 km downscaled reanalyses (BARRA-C) are assessed for their added skill over BARRA-R and global reanalyses for near-surface parameters (temperature, wind, and precipitation) at observation locations and against independent 5 km gridded analyses. BARRA-C demonstrates better agreement with point observations for temperature and wind, particularly in topographically complex regions and coastal regions. BARRA-C also improves upon BARRA-R in terms of the intensity and timing of precipitation during the thunderstorm seasons in NSW and spatial patterns of sub-daily rain fields during storm events. BARRA-C reflects known issues of CPMs: overestimation of heavy rain rates and rain cells, as well as underestimation of light rain occurrence. As a hindcast-only system, BARRA-C largely inherits the domain-averaged bias pattern from BARRA-R but does produce different climatological extremes for temperature and precipitation. An added-value analysis of temperature and precipitation extremes shows that BARRA-C provides additional skill over BARRA-R when compared to gridded observations. The spatial patterns of BARRA-C warm temperature extremes and wet precipitation extremes are more highly correlated with observations. BARRA-C adds value in the representation of the spatial pattern of cold extremes over coastal regions but remains biased in terms of magnitude.

Which Reanalysis Dataset Should We Use for Renewable Energy Analysis in Ireland?

Attention should always be given to which reanalysis dataset to use when preparing analysis for a project. The accuracies of three reanalysis datasets, two global (ERA5 and MERRA-2) and one high-resolution regional reanalysis (MÉRA), are assessed by comparison with observations at seven weather observing stations around Ireland. Skill scores are calculated for the weather variables at these stations that are most relevant to the renewable energy sector: 10 m wind for wind power; surface shortwave radiation (SW) and 2 m temperature for photovoltaic power generation. The choice of which reanalysis dataset to use is important when future planning depends on this data. The newer ERA5 generally outperforms the other two reanalyses. However, this is not always true, and the best performing reanalysis dataset often depends on the variable of interest and location. As errors are significant for these reanalysis datasets, consideration should also be given to datasets specifically tailored to renewable energy resource modelling.

Evaluation of Precipitation in the Chinese Regional Reanalysis Using Satellite Estimates, Gauge-Based Observations and Global Reanalysis

Two high-resolution Chinese regional reanalysis (CNRR) datasets at a resolution of 18 km during the period of 1998–2009 are generated by Gridpoint Statistical Interpolation (GSI) data assimilation system and spectral nudging (SN) method. The precipitation from CNRR is comprehensively evaluated against the observational datasets and global reanalysis ERA5 over East-Asia. The climatology mean, seasonal variability, extreme events, and summer diurnal cycle of precipitation are analyzed. Results show that CNRR reasonably reproduces the observed characteristics of rainfall, although some biases exist. The spatial distribution of climatology mean precipitation is well simulated by CNRR, while overestimation exists especially on the west side of Tibetan-Plateau (TP). CNRR reproduces the unimodal feature of the annual cycle with overestimations of summer precipitation, and well produces the probability of light and moderate rainfall but tend to overestimate heavy and extreme precipitation over most regions in China. The overall spatial distribution of extreme precipitation indices can be captured by CNRR. The diurnal cycle of summer precipitation, as well as the amplitude of diurnal cycle, are better reproduced by CNRR-GSI, capturing eastward propagation of diurnal phase from TP along the Yangtze River. CNRR-GSI generally outperforms CNRR-SN over most regions of China except in reproducing heavy and extreme rainfall in the Yangtze River Basin (YRB) and South China (SC) regions. CNRR-GSI shows comparable results with the latest ERA5 and outperforms it in simulating the diurnal cycle of precipitation. This dataset can be considered as a reliable source for precipitation related applications.

IMDAA: High Resolution Satellite-era Reanalysis for the Indian Monsoon Region

AbstractA high resolution regional reanalysis of the Indian Monsoon Data Assimilation and Analysis (IMDAA) project is made available to researchers for deeper understanding of the Indian monsoon and its variability. This 12 km resolution reanalysis covering the satellite-era from 1979 to 2018 using 4D-Var data assimilation method and the UK Met Unified Model is presently the highest resolution atmospheric reanalysis carried out for the Indian monsoon region. Conventional and satellite observations from different sources are used, including Indian surface and upper air observations, of which some were not used in any previous reanalyses. Various aspects of this reanalysis, like quality control and bias correction of observations, data assimilation system, land surface analysis, and verification of reanalysis products, are presented in this paper. Representation of important weather phenomena of each season over India in the IMDAA reanalysis verifies reasonably well against India Meteorological Department (IMD) observations and compares closely with ERA5. Salient features of the Indian summer monsoon are found to be well represented in the IMDAA reanalysis. Characteristics of major semi-permanent summer monsoon features (e.g., Low-level Jet and Tropical Easterly Jet) in IMDAA reanalysis are consistent with ERA5. The IMDAA reanalysis has captured the mean, inter-annual, and intra-seasonal variability of summer monsoon rainfall fairly well. IMDAA produces a slightly cooler winter and a hotter summer than the observations; the reverse for ERA5. IMDAA captured the fine-scale features associated with a notable heavy rainfall episode over complex terrain. In this study, the fine grid spacing nature of IMDAA is compromised due to the lack of comparable resolution observations for verification.

On the diurnal cycle and variability of winds in the lower planetary boundary layer: evaluation of regional reanalyses and hindcasts

To accurately calculate the impact of renewables on power production in complex electric power grids, high-resolution and ideally seamless data within the planetary boundary layer are required. Therefore, the quality of different regional reanalyses and hindcasts is evaluated with respect to the representation of the planetery boundary layer and related sub-daily processes. On the one hand, high resolution regional reanalysis from the UERRA (UE-SMHI, UE-UKMO) and a similar project (COSMO-REA6) are considered. On the other hand, two hindcasts based on the COSMO-REA6 configuration are included in this study, i.e. a simulation with perfect boundaries and a simulation additionally utilizing spectral nudging. The focus of the evaluation is on measurements at four flux towers that are not part of any assimilation procedure. In this paper, we will show that the model’s quality depends on both the complete model system – assimilation method, resolution and physical parameterization – as well as on the performance measure. The daily cycle is best depicted by the hindcasts and even COSMO-REA6 hardly introduces spurious variability. UE-SMHI (3D-Var) suffers from spin-up in particular visible at the elevated levels, whereas the spin-up is damped in UE-UKMO (4D-Var). Investigation of atmospheric stability reveals that diurnal variation of stratification is for the most part well reproduced, but strong deficits were found for all COSMO simulations in reproducing strong stratification and corresponding wind speed gradients. Moreover, an overestimation of superadiabatic lapse rates and corresponding overly weak turbulent mixing is found for UE-UKMO. Furthermore, a combination of ramp statistics and contingency tables is utilized to detect a clear advantage of sophisticated assimilation systems over hindcasts. The evaluation framework presented underpins the importance of ramp statistics and vertical measurement profiles, especially with respect to assessing long-term simulations.

Verification of moist surface variables over northern Australia in a high-resolution reanalysis (BARRA)

Reanalyses are important tools for understanding past weather and climate variability, but detailed verification of near surface humidity variables have not been published. This is particularly concerning in tropical regions where humid conditions impact meteorology and human activities. In this study, we used screen level temperature and humidity data from a high-resolution atmospheric regional reanalysis, the Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia (BARRA), validated against automatic weather stations (AWS) data for 32 sites across northern Australia. Overall, the BARRA data was reliable, with the time series from the AWS and BARRA being very highly correlated, but there were some seasonal and diurnally varying biases. The variability of the differences also changed from location to location and as a function of time of day and season, but much less than the biases. This variability was less than the ‘weather signal’ as evidenced by the high correlations. In particular, the amplitude of the diurnal cycle was overestimated, particularly in the dry (winter) season. In general, the differences in temperature were larger than those of the dew point temperature, and the wet bulb temperature had the least uncertainty. Overall, this study contributes to a better understanding of the effectiveness of reanalyses for examining the impact of moist variables on tropical climate variability.

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 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.

Temperature monitoring in mountain regions using reanalyses: lessons from the Alps

The monitoring of near-surface temperature is a fundamental task of climatology that remains especially challenging in mountain regions. Here we assess the regional monitoring capabilities of modern reanalysis products in the well-monitored northern Swiss Alps during the last 20–60 years. Monthly and seasonal 2 m air temperature (T2m) anomalies the global ERA5 and the three regional reanalysis products HARMONIE, MESCAN-SURFEX and COSMO-REA6 are evaluated against high quality in situ observational data for a low elevation (foothills) mean, and a high elevation (Alpine) mean. All reanalysis products show a good year-round performance for the foothills with the global reanalysis ERA5 showing the best overall performance. The high-resolution regional reanalysis COSMO-REA6 clearly performs best for the Alpine mean, especially in winter. Most reanalysis data sets show deficiencies at high elevations in winter and considerably overestimate recent T2m trends in winter. This stresses the fact that even in the most recent decades utmost care is required when using reanalysis data for near-surface temperature trend assessments in mountain regions. Our results indicate that a high-resolution model topography is an important prerequisite for an adequate monitoring of winter T2m using reanalysis data at high elevations in the Alps. Assimilating T2m remains challenging in highly complex terrain. The remaining shortcomings of modern reanalyses also highlight the continued need for a reliable and dense in situ observational monitoring network in mountain regions.