ECMWF Re-Analysis Research Articles

Analysis of CMIP6 atmospheric moisture fluxes and the implications for projections of future change in mean and heavy rainfall

AbstractMotivated by the wide range of projections of regional rainfall (or precipitation, pr) based on the CMIP5 ensemble of global climate models, we investigate the atmospheric moisture budget of idealized rising‐CO2 (1pctCO2) simulations from 10 models submitted to CMIP6. We use the new CMIP6 standard variables, the vertically integrated eastward and northward moisture flux, and provide a multi‐model assessment of these, their convergence (conv), along with water vapour path (prw). Seasonal climatologies of a 20‐year base climate for pr, prw, flux, and conv match well those from the ECMWF's Reanalysis 5. In addition, composites ‘H’ of quantities for months in the top decile of monthly rainfall, calculated at each model grid point, produce a field of heavy rainfall, on average double that of the mean. Spatial correlation coefficients (r) between the pr and conv fields are typically .7 in each season. This rises to .9 for the corresponding H fields, due to the important link between moisture convergence and heavy rainfall. Fields of standardized change (Δ), or change per degree of global warming, were calculated. The ensemble‐mean Δpr is only weakly correlated with the base climate pr, and likewise for conv. However, the prw change is highly correlated with the base prw, pointing to the importance of the “thermodynamic” change in that variable. Globally, the net Δconv is highly correlated with Δpr, around .9. This rises to .95 for the heavy‐rain composites. Relationships between changes in pr and other components hold at grid points also, when the individual model results are correlated. Clearly, there is a dependence in the change in pr with that in conv, particularly for heavy rainfall. Assessment of a larger ensemble from CMIP6, using the new flux variables, would improve the evaluation of these processes, and potentially allow more confident projections in future rainfall changes.

Analysis of various thermodynamic instability parameters and their association with the rainfall during thunderstorm events over Anakapalle (Visakhapatnam district), India

Thunderstorm events usually take place in cumulonimbus clouds which are complemented with intense rainfall and high-speed winds. In general, rainfall parameter has massive significance when compared to other parameters. In this paper, a group of thunderstorm-related stability parameters were analyzed for pre-monsoon season only. Later, we also tried to study the association between thunderstorm-related stability parameters and rainfall parameter in pre-monsoon season over Anakapalle (Visakhapatnam district) during 2001–2010. We have utilized ERA-Interim ECMWF reanalysis daily datasets for this study. We also tried to compare IMD thunderstorm occurrence days with NOAA CPC-calculated rainfall days in pre-monsoon season over Anakapalle region for every year during 2001–2010. Out of those parameters, upward vertical velocity, convective available potential energy, K-index (KI), humidity index and total totals index parameters have shown good thresholds supporting the rainfall activity during pre-monsoon season. Later, we have also attempted the prediction of DCI and KI parameters over Anakapalle region using artificial neural network (ANN) and auto-regressive moving average (ARMA) techniques. In comparison between the two techniques, ANN technique has shown good correlation with ERA-Interim ECMWF reanalysis data.

Capacity of Satellite-Based and Reanalysis Precipitation Products in Detecting Long-Term Trends across Mainland China

Despite numerous assessments of satellite-based and reanalysis precipitation across the globe, few studies have been conducted based on the precipitation linear trend (LT), particularly during daytime and nighttime, when there are different precipitation mechanisms. Herein, we first examine LTs for the whole day (LTwd), daytime (LTd), and nighttime (LTn) over mainland China (MC) in 2003–2017, with sub-daily observations from a dense rain gauge network. For MC and ten Water Resources Regions (WRRs), annual and seasonal LTwd, LTd, and LTn were generally positive but with evident regional differences. Subsequently, annual and seasonal LTs derived from six satellite-based and six reanalysis popular precipitation products were evaluated using metrics of correlation coefficient (CC), bias, root-mean-square-error (RMSE), and sign accuracy. Finally, metric-based optimal products (OPs) were identified for MC and each WRR. Values of each metric for annual and seasonal LTwd, LTd, or LTn differ among products; meanwhile, for any single product, performance varied by season and time of day. Correspondingly, the metric-based OPs varied among regions and seasons, and between daytime and nighttime, but were mainly characterized by OPs of Tropical Rainfall Measuring Mission (TRMM) 3B42, ECMWF Reanalysis (ERA)-Interim, and Modern Era Reanalysis for Research and Applications (MERRA)-2. In particular, the CC-based (RMSE-based) OPs in southern and northern WRRs were generally TRMM3B42 and MERRA-2, respectively. These findings imply that to investigate precipitation change and obtain robust related conclusions using precipitation products, comprehensive evaluations are necessary, due to variation in performance within one year, one day and among regions for different products. Additionally, our study facilitates a valuable reference for product users seeking reliable precipitation estimates to examine precipitation change across MC, and an insight (i.e., capacity in detecting LTs, including daytime and nighttime) for developers improving algorithms.

Ensemble Downscaling of Soil Moisture Data Using BMA and ATPRK

Soil moisture is essential information for meteorological and hydrological analyses. To date, many efforts have been made to achieve the two goals for soil moisture data, i.e., the improvement of accuracy and resolution, which is very challenging. We presented an ensemble downscaling method for quality improvement of gridded soil moisture data in terms of the accuracy and the spatial resolution by the integration of BMA (Bayesian model averaging) and ATPRK (area-to-point regression kriging). In the experiments, the BMA ensemble showed a 22% better accuracy than the data sets from ESA CCI (European Space Agency-Climate Change Initiative), ERA5 (ECMWF Reanalysis 5), and GLDAS (Global Land Data Assimilation System) in terms of RMSE (root mean square error). Also, the ATPRK downscaling could enhance the spatial resolution from 0.25o to 0.05o while preserving the improved accuracy and the spatial pattern of the BMA ensemble, without under- or over-estimation. The quality-improved data sets can contribute to a variety of local and regional applications related to soil moisture, such as agriculture, forest, hydrology, and meteorology. Because the ensemble downscaling method can be applied to the other land surface variables such as temperature, humidity, precipitation, and evapotranspiration, it can be a viable option to complement the accuracy and the spatial resolution of satellite images and numerical models.

Australian Rainfall Anomalies in 2018–2019 Linked to Indo‐Pacific Driver Indices Using ERA5 Reanalyses

AbstractThe 2019 and 2018–2019 periods had record low All‐Australia rainfall in both observations and the ECMWF's Reanalysis 5, or ERA5, data set over 1979–2019. An analysis of the relationships between interannual variability of rainfall and atmospheric circulation, vertically integrated moisture flux, and temperature anomalies using ERA5 alone is undertaken. Both standard driver indices and those that combine the Pacific and Indian Ocean influences are used. Regression fields for low annual Australian rainfall show a widespread negative rainfall anomaly extending into the east Indian Ocean or IND region and a positive anomaly in the western Pacific PAC region. A moisture flux anomaly takes moisture eastwards toward PAC. This pattern largely persists in the four seasons. In March–May 2019 it can be partially linked to a positive Niño 3.4 anomaly and in June–August to a positive Indian Ocean Dipole. For the annual case, the detrended Niño 4 index explains 24% of the 2019 ERA5 Australian rainfall deficit. This rises to 38% for the 2018–2019 deficit (46% for observations), for the Pacific‐Indian Dipole index that combines the PAC and IND regions, whose sea surface temperature anomalies have opposite effects. The correlation of the index with 2‐year rainfall is −0.7. There is much variability, in the Australian monsoon rainfall especially, that is not linked to simple indices, although rainfall is well matched with moisture flux convergence. This extends to the equatorial zone where the climate of the Maritime Continent can be better examined with ERA5's 0.25° data.

Uncertainties in Ocean Latent Heat Flux Variations over Recent Decades in Satellite-Based Estimates and Reduced Observation Reanalyses

AbstractFour state-of-the-art satellite-based estimates of ocean surface latent heat fluxes (LHFs) extending over three decades are analyzed, focusing on the interannual variability and trends of near-global averages and regional patterns. Detailed intercomparisons are made with other datasets including 1) reduced observation reanalyses (RedObs) whose exclusion of satellite data renders them an important independent diagnostic tool; 2) a moisture budget residual LHF estimate using reanalysis moisture transport, atmospheric storage, and satellite precipitation; 3) the ECMWF Reanalysis 5 (ERA5); 4) Remote Sensing Systems (RSS) single-sensor passive microwave and scatterometer wind speed retrievals; and 5) several sea surface temperature (SST) datasets. Large disparities remain in near-global satellite LHF trends and their regional expression over the 1990–2010 period, during which time the interdecadal Pacific oscillation changed sign. The budget residual diagnostics support the smaller RedObs LHF trends. The satellites, ERA5, and RedObs are reasonably consistent in identifying contributions by the 10-m wind speed variations to the LHF trend patterns. However, contributions by the near-surface vertical humidity gradient from satellites and ERA5 trend upward in time with respect to the RedObs ensemble and show less agreement in trend patterns. Problems with wind speed retrievals from Special Sensor Microwave Imager/Sounder satellite sensors, excessive upward trends in trends in Optimal Interpolation Sea Surface Temperature (OISST AVHRR-Only) data used in most satellite LHF estimates, and uncertainties associated with poor satellite coverage before the mid-1990s are noted. Possibly erroneous trends are also identified in ERA5 LHF associated with the onset of scatterometer wind data assimilation in the early 1990s.

Artificial Neural Networks to Retrieve Land and Sea Skin Temperature from IASI

Surface skin temperature (Tskin) derived from infrared remote sensors mounted on board satellites provides a continuous observation of Earth’s surface and allows the monitoring of global temperature change relevant to climate trends. In this study, we present a fast retrieval method for retrieving Tskin based on an artificial neural network (ANN) from a set of spectral channels selected from the Infrared Atmospheric Sounding Interferometer (IASI) using the information theory/entropy reduction technique. Our IASI Tskin product (i.e., TANN) is evaluated against Tskin from EUMETSAT Level 2 product, ECMWF Reanalysis (ERA5), SEVIRI observations, and ground in situ measurements. Good correlations between IASI TANN and the Tskin from other datasets are shown by their statistic data, such as a mean bias and standard deviation (i.e., [bias, STDE]) of [0.55, 1.86 °C], [0.19, 2.10 °C], [−1.5, 3.56 °C], from EUMETSAT IASI L-2 product, ERA5, and SEVIRI. When compared to ground station data, we found that all datasets did not achieve the needed accuracy at several months of the year, and better results were achieved at nighttime. Therefore, comparison with ground-based measurements should be done with care to achieve the ±2 °C accuracy needed, by choosing, for example, a validation site near the station location. On average, this accuracy is achieved, in particular at night, leading to the ability to construct a robust Tskin dataset suitable for Tskin long-term spatio-temporal variability and trend analysis.

An Evaluation of the Wind Energy Resources along the Spanish Continental Nearshore

The main objective of the present work is to provide a comprehensive picture of the wind conditions in the Spanish continental nearshore considering a state-of-the-art wind dataset. In order to do this, the ERA5 wind data, covering the 20-year time interval from 1999 to 2018, was processed and evaluated. ERA stands for ’ECMWF Re-Analysis’ and refers to a series of research projects at ECMWF (European Centre for Medium-Range Weather Forecasts) which produced various datasets. In addition to the analysis of the wind resources (reported for a 100 m height), the performances of several wind turbines, ranging from 3 to 9.5 MW, were evaluated. From the analysis of the spatial maps it was observed that the Northern part of this region presents significant wind resources, the mean wind speed values exceeding 9 m/s in some locations. On the other hand, in regard to the Southern sector, more energetic conditions are visible close to the Strait of Gibraltar and to the Gulf of Lion. Nevertheless, from the analysis of the data corresponding to these two Southern nearshore points it was observed that the average wind speed was lower than 8 m/s in both summer and winter months. Regarding the considered wind turbines, the capacity factor did in general not exceed 20%—however, we did observe some peaks that could reach to 30%. Finally, it can be highlighted that the Northern part of the Spanish continental nearshore is significant from the perspective of extracting offshore wind energy, especially considering the technologies based on floating platforms. Furthermore, because of the clear synergy between wind and wave energy, which are characteristic to this coastal environment, an important conclusion of the present work is that the implementation of joint wind–wave projects might be effective in the Northwestern side of the Iberian nearshore.

The influence of boreal summer intra-seasonal oscillations on Indo-western Pacific Ocean surface waves

The present study examines the influence of Boreal Summer Intra-Seasonal Oscillation (BSISO) on Tropical Indian Ocean surface waves using the latest version of ECMWF reanalysis (ERA5) during summer monsoon months June through August (JJA). BSISO is a distinct mode of ISO during JJA having a northward and eastward movement from the equatorial Indian Ocean to the western Pacific Ocean. Composite analysis of anomalies of significant wave heights (SWH), wind sea, swell, and mean wave period for 8 phases of BSISO has been carried out to understand its influence. SWH anomalies in response to BSISO’s are phase-dependent. Negative SWH anomalies are noticed with strong northward and weak eastward propagation during the phases 1–3 in response to the easterly wind anomalies over the north Indian Ocean (NIO). During phases 5–7, high positive SWH anomalies (~ 0.5 m) in response to the westerly wind anomalies with northward and weak eastward propagation over NIO. Phases 4 and 8 behave like transition phases. In addition, enhanced (suppressed) SWH anomalies (~ 0.5 m) are seen during the active (break) spells of BSISO over NIO. Over the southern tip of India, negative (positive) SWH anomalies prevail during the active (break) conditions. This study clearly suggests that the wave forecast advisories during intra-seasonal time scales would be more useful for offshore and coastal activities during the summer monsoon.

Precipitation Merging Based on the Triple Collocation Method Across Mainland China

Triple collocation (TC) is a novel method for quantifying the uncertainties of three data sets with mutually independent errors and has been widely used over different geographical fields. Researches in recent years report that TC shows potential in merging multiple data sets from different sources, while the TC-based merging method has not been used over precipitation. Using the TC formulation, this study merges precipitation from the Climate Prediction Center's morphing technique (CMORPH), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), and the fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA5). The interim ECMWF Re-Analysis (ERA-Interim) is also involved to act as the substitute of ERA5 in some specific experiments for quality comparison between them. Merged data sets are produced at 0.25 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">°</sup> ×0.25 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">°</sup> and daily resolutions from March 2000 to December 2013 over Mainland China, using ground observations from more than 2000 rain gauges as the validation benchmark. First, the effectiveness of the TC-based method for precipitation merging is assessed. Then, two weighting methods using root-mean-square error (RMSE) in logarithmic scale (log-RMSE) and modified scale (mod-RMSE) are compared because previous studies show that mod-RMSE is more suitable for characterizing errors within estimated data. Meanwhile, two merging strategies are designed, that is, merging rainfall and snowfall separately (RS) and merging precipitation directly (P). The results show that 1) all the merged products are superior to any input product which proves that the TC method is effective in precipitation merging; 2) TC-based merging generally has a better performance than dynamic Bayesian model averaging (DBMA)-based merging; 3) mod-RMSE shows worse performance in weight estimation than log-RMSE because mod-RMSE will deteriorate the impact of the underestimated inputs; and 4) RS-based merging is superior to P-based merging, and the superiority is particularly notable in winter. The RS strategy will be very helpful in improving the accuracy of precipitation estimates in cold climate such as over mountainous and high-altitude regions. Finally, the limitations of the TC method and potential solutions are discussed. This study demonstrates the great potential of the TC-based merging method in precipitation and provides insights into its application and development.

Evaluation and integration of reanalysis rainfall products under contrasting climatic conditions in India

Advancements in science and technology lead to the development of different sensors that measure precipitation, an intrinsic component of the hydrological cycle. As conventional rainfall measurement is tedious, satellite precipitation products are being developed. In the current study, the newly released ECMWF (European Centre for Medium-Range Weather Forecasts) ReAnalysis (ERA-5) along with the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) dataset was statistically evaluated against Indian Meteorological Department (IMD) gridded data and was employed for streamflow simulations using the Soil and Water Assessment Tool (SWAT) model in five contrasting climatic regions of India. Distinct calibration scenarios were developed to test the performance of these reanalysis datasets for simulating streamflow. From categorical and continuous statistical results, ERA-5 exhibited better performance in detecting low (0–5 mm/h) and medium intensity (6–25 mm/h) rainfall, whereas CHIRPS manifested better performance in detecting high intensity rainfall events (>25 mm/h). CHIRPS proved to be effective in simulating streamflows in three out of five basins, whereas IMD exhibited better performance in the other two basins leaving ERA-5 with poor performance for streamflow simulations in all the basins. It was also observed that Satellite Precipitation Products (SPP's) are accurate in humid and tropical regions when compared to arid and semi-arid regions. The worst performance was exhibited in the Ponnaiyar river basin by SPP's and hence bias correction and integration techniques were applied to improve the performance of streamflow simulations further. The novel integration of SPP employing nudging scheme yielded better streamflow simulations when forced into SWAT hydrological model compared to the streamflow simulations obtained when loaded with bias-corrected and raw datasets. The implemented nudging scheme improved the performance of streamflow simulations and hence can be implemented in any basin that is ungauged or resulting in poor hydrological modeling performance when employed with one SPP. The adopted nudging scheme will be highly useful to generate long-term consistent precipitation records in an ungauged or poorly gauged river basin.

Climatology of Tropospheric Relative Humidity over the Korean Peninsula from Radiosonde and ECMWF Reanalysis

Conventional radiosondes can be used to measure the relative humidity over liquid (RHL) by assuming a saturated vapor pressure over the liquid. However, this assumption results in significant errors with respect to measurements in the upper troposphere, where the effect of ice is dominant. Therefore, this study presents a novel method that considers the effects of ice to determine the relative humidity from radiosonde RHL data for the last 40 years (1979–2018) over the upper layers of the Korean peninsula. Even though the relative humidity obtained from the reanalysis data was significantly different from the radiosonde-based RHL, the difference was much reduced when relative humidity was calculated using the novel method proposed in this study. Such improvements in the estimated relative humidity could be attributed to the consideration of the ice effect at temperatures above freezing level. Additionally, the validity of the relative humidity estimated in this study was established based on a two-week case analysis of data from Boseong station. Furthermore, two peak relative humidity modes for the lower and upper layers were clearly identified in the mean climatology profiles, which indirectly suggested the absence of mid-level clouds around the 700-hPa level and 500-hPa level in winter and summer, respectively. This study is meaningful as it is the first study to determine the relative humidity distribution over the Korean peninsula using radiosonde observations. The scientific value obtained can potentially be expanded by applying the proposed method to other radiosonde observation networks, which are widely distributed worldwide.

Representation of low-tropospheric temperature inversions in ECMWF reanalyses over Europe

Despite the fact that tropospheric temperature inversions are thought to be an important feature of climate as well as a significant factor affecting air quality, low-level cloud formation, and the radiation budget of the Earth, a quantitative assessment of their representation in atmospheric reanalyses is yet missing. Here, we provide new evidence of the occurrence of low-tropospheric temperature inversions and associated uncertainties in their parameters existing among reanalyses produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) and upper-air soundings for Europe covering the period 2001–2010. The reanalyses utilized here include (1) surface-input reanalyses represented by ERA-20C and CERA-20C as well as (2) full-input reanalyses represented by ERA-Interim and ERA5. The upper-air soundings were derived from the Integrated Global Radiosonde Archive (IGRA), version 2. The data consists mainly of air temperature and geopotential height from the model levels (ModLev) and pressure levels (PresLev) of ECMWF reanalyses. The results show that the frequency of surface-based inversions (SBI) and elevated inversions (EI) is largely in agreement among the reanalyses. The quality of their representation depends, however, on the inversion type, season, and region. Over the vast majority of IGRA upper-air stations, SBI frequency is overestimated and EI frequency is underestimated by ECMWF reanalyses. Substantially larger uncertainties arise from the selection between the data of ModLev and PresLev of the reanalyses—the differences in the frequency of the temperature inversions are particularly large for summertime SBI suggesting that PresLev are not capable of resolving the main features of shallow and weak SBI.

Ambient conditions prevailing during hail events in central Europe

Abstract. Around 26 000 severe convective storm tracks between 2005 and 2014 have been estimated from 2D radar reflectivity for parts of Europe, including Germany, France, Belgium, and Luxembourg. This event set was further combined with eyewitness reports, environmental conditions, and synoptic-scale fronts based on the ERA-Interim (ECMWF Reanalysis) reanalysis. Our analyses reveal that on average about a quarter of all severe thunderstorms in the investigation area were associated with a front. Over complex terrains, such as in southern Germany, the proportion of frontal convective storms is around 10 %–15 %, while over flat terrain half of the events require a front to trigger convection. Frontal storm tracks associated with hail on average produce larger hailstones and have a longer track. These events usually develop in a high-shear environment. Using composites of environmental conditions centered around the hailstorm tracks, we found that dynamical proxies such as deep-layer shear or storm-relative helicity become important when separating hail diameters and, in particular, their lengths; 0–3 km helicity as a dynamical proxy performs better compared to wind shear for the separation. In contrast, thermodynamical proxies such as the lifted index or lapse rate show only small differences between the different intensity classes.

Satellite Based Interpretation of Stability Parameters on Convective Systems over India and Srilanka

India is frequently exposed to convective systems (CS) during the hot period of pre-monsoon season. Huge thunderstorms are more often over India in the recent years. To analyze these systems, the stability parameters considered as good precursors for understanding the physics of the convective systems in prior to their occurrence. The present study is concerned about the analysis of stability parameters such as K Index (KI), Lifted Index (LI), Total Totals Index (TTI), Humidity Index (HI), Convective available potential energy (CAPE), Convective Inhibition (CIN), Lifting Condensation Level (LCL) and Level of free Convection (LFC) associated with severe convective systems over India and Srilanka during April, 2015. First, INSAT-3D satellite based brightness temperature (BT) product was used for analyzing the initial, mature and dissipation stages of the convective system at every 30 min interval. The data products from MODIS Terra satellite, MODIS Aqua satellite, Era-Interim ECMWF reanalysis satellite data and IASI satellite were used for understanding the thermodynamics related to the occurrence of these convective systems. The results of stability parameters that were derived from MODIS Terra and Aqua satellite’s data products indicated the favorable threshold values for the occurrence of convective systems. Results from Era-Interim ECMWF reanalysis satellite data also helps us to understand the dynamics involved behind the convective system. The results from IASI satellite also helped us to understand the severity of convective systems during cloudy situations. The results of this study impulse the need of satellites for analyzing the convective systems prior to their occurrence.

Estimation of Potato Yield Using Satellite Data at a Municipal Level: A Machine Learning Approach

Crop growth modeling and yield forecasting are essential to improve food security policies worldwide. To estimate potato (Solanum tubersum L.) yield over Mexico at a municipal level, we used meteorological data provided by the ERA5 (ECMWF Re-Analysis) dataset developed by the Copernicus Climate Change Service, satellite imagery from the TERRA platform, and field information. Five different machine learning algorithms were used to build the models: random forest (rf), support vector machine linear (svmL), support vector machine polynomial (svmP), support vector machine radial (svmR), and general linear model (glm). The optimized models were tested using independent data (2017 and 2018) not used in the training and optimization phase (2004–2016). In terms of percent root mean squared error (%RMSE), the best results were obtained by the rf algorithm in the winter cycle using variables from the first three months of the cycle (R2 = 0.757 and %RMSE = 18.9). For the summer cycle, the best performing model was the svmP which used the first five months of the cycle as variables (R2 = 0.858 and %RMSE = 14.9). Our results indicated that adding predictor variables of the last two months before the harvest did not significantly improved model performances. These results demonstrate that our models can predict potato yield by analyzing the yield of the previous year, the general conditions of NDVI, meteorology, and information related to the irrigation system at a municipal level.

Climate Variability and Malaria over West Africa.

Malaria is a major public health problem in West Africa. Previous studies have shown that climate variability significantly affects malaria transmission. The lack of continuous observed weather station data and the absence of surveillance data for malaria over long periods have led to the use of reanalysis data to drive malaria models. In this study, we use the Liverpool Malaria Model (LMM) to simulate spatiotemporal variability of malaria in West Africa using daily rainfall and temperature from the following: Twentieth Century Reanalysis (20th CR), National Center for Environmental Prediction (NCEP), European Centre for Medium-Range Weather Forecasts (ECMWF) Atmospheric Reanalysis of the Twentieth Century (ERA20C), and interim ECMWF Re-Analysis (ERA-Interim). Malaria case data from the national surveillance program in Senegal are used for model validation between 2001 and 2016. The warm temperatures found over the Sahelian fringe of West Africa can lead to high malaria transmission during wet years. The rainfall season peaks in July to September over West Africa and Senegal, and the malaria season lasts from September to November, about 1-2 months after the rainfall peak. The long-term trends exhibit interannual and decadal variabilities. The LMM shows acceptable performance in simulating the spatial distribution of malaria incidence. However, some discrepancies are found. These results are useful for decision-makers who plan public health and control measures in affected West African countries. The study would have substantial implications for directing malaria surveillance activities and health policy. In addition, this malaria modeling framework could lead to the development of an early warning system for malaria in West Africa.

Multisource Assessments of the FengYun-3D Microwave Humidity Sounder (MWHS) On-Orbit Performance

The microwave humidity sounder (MWHS) onboard the Fengyun-3D satellite is providing the data for profiling atmospheric temperature and moisture and has become an important data source for improving the weather forecasts. In this article, three data sources are utilized for assessing the MWHS on-orbit performance, including Global Navigation Satellite System Occultation Sounder (GNOS), ECMWF (European Centre for Medium-Range Weather Forecasts) Re-Analysis (ERA)-Interim reanalysis, and Advanced Technology Microwave Sounder (ATMS) data. GNOS-retrieved atmospheric profiles and the reanalysis data are used as inputs to the community radiative transfer model (CRTM) for simulating the MWHS brightness temperatures at the top of the atmosphere in July 2018 for characterizing the instrument performance. Since ATMS is a well-calibrated microwave sounding instrument onboard both Suomi NPP and NOAA-20 satellites, its measurements are also collocated with MWHS data for a consensus analysis using the simultaneous nadir overpasses (SNOs) method. In comparing GNOS simulations, MWHS upper air temperature sounding channels (3–6) have relatively larger biases (less than 2.5 K) than the water vapor sounding channels. However, the standard deviation of the difference between observations and simulations (O-B) is larger for water vapor sounding channels. For ERA simulations, MWHS sounding channels exhibit negative biases similar to GNOS results but the standard deviation of O-B at the water vapor channels is much smaller. When compared with ATMS water vapor channels, MWHS biases are mostly negative and agree with those from ERA simulation. Thus, the large uncertainty in simulating MWHS water vapor sounding channels from GNOS could result from the poor input water vapor profiles and high water vapor variability in the lower troposphere.

Towards an objective historical tropical cyclone dataset for the Australian region

The appropriate design of infrastructure in tropical cyclone (TC) prone regions requires an understanding of the hazard risk profile underpinned by an accurate, homogenous long-term TC dataset. The existing Australian region TC archive, or ‘best track’ (BT), suffers from inhomogeneities and an incomplete long-term record of key TC parameters. This study assesses mostly satellite-based objective techniques for 1981–2016, the period of a geostationary satellite imagery dataset corrected for navigation and calibration issues. The satellite-based estimates of Australian-region TCs suffer from a general degradation in the 1981–1988 period owing to lower quality and availability of satellite imagery.The quality of the objective techniques for both intensity and structure is compared to the reference BT 2003–2016 estimates. For intensity the Advanced Dvorak Technique algorithm corresponds well with the BT 2003–2016, when the algorithm can use passive microwave data (PMW) as an input. For the period prior to 2003 when PMW data is unavailable, the intensity algorithm has a low bias. Systematic corrections were made to the non-PMW objective estimates to produce an extended (1989–2016) homogeneous dataset of maximum wind that has sufficient accuracy to be considered for use where a larger homogeneous sample size is valued over a shorter more accurate period of record. An associated record of central pressure using the Courtney-Knaff-Zehr wind pressure relationship was created.For size estimates, three techniques were investigated: the Deviation Angle Variance and the ‘Knaff’ techniques (IR-based), while the ‘Lok’ technique used model information (ECMWF reanalysis dataset and TC vortex specification from ACCESS-TC). However, results lacked sufficient skill to enable extension of the reliable period of record. The availability of scatterometer data makes the BT 2003–2016 dataset the most reliable and accurate. Recommendations regarding the best data source for each parameter for different periods of the record are summarised.

Random forest regression for improved mapping of solar irradiance at high latitudes

Datasets from meteorological reanalyses and retrievals from satellites are the available sources of large-scale information about solar radiation. However, both the reanalyses and the satellite-based estimates can be severely biased, especially in high latitude regions. In this study, surface solar irradiance estimates from the ECMWF Reanalysis 5 (ERA5) and the Cloud, Albedo, Radiation dataset Edition 2 (CLARA-A2) were used as input to a random forest regression (RFR) model to construct a novel dataset with higher accuracy and precision than the input datasets. For daily averages of global horizontal irradiance (GHI) at Norwegian sites, CLARA-A2 and ERA5 respectively produced a root mean squared deviation (RMSD) of 17.9 Wm−2 and 27.1 Wm−2, a mean absolute deviation (MAD) of 11.9 Wm−2 and 17.5 Wm−2, and a bias of −1.5 Wm−2 and 4.3 Wm−2. In contrast, the proposed regression model provided an RMSD of 16.2 Wm−2, an MAD of 10.8 Wm−2, and a bias of 0.0 Wm−2. This shows that the RFR model is both accurate and precise, and significantly reduces both dispersion and bias in the new dataset with respect to the constituent sources. A sky-stratification analysis was performed and it was found that the proposed model provides better estimates under all sky conditions with particular improvements in intermediate-cloudy conditions. The proposed regression model was also tested on five Swedish locations and it was found to improve surface solar irradiance estimates to a similar degree as for the Norwegian locations, thus proving its consistency under similar climatic conditions.