Rainfall Rate Research Articles

In Situ Experimental Study of Cloud-Precipitation Interference by Low-Frequency Acoustic Waves

Since acoustic agglomeration is an effective pre-treatment technique for removing fine particles, it can be considered as a potential technology for applications in aerosol pollution control, industrial dust and mist removal, and cloud and precipitation interference. In this study, the cloud-precipitation interference effect was evaluated in situ based on a multi-dimensional multi-scale monitoring system. The variations in the spatial and temporal distribution of rainfall near the surface and the characteristics of precipitation droplets in the air were investigated. The results indicate that strong low-frequency acoustic waves had a significant impact on the macro-characteristics of rainfall clouds, the microphysical structure of rain droplets and near-surface precipitation, and various microwave parameters. In terms of physical structure, the precipitation cloud’s base height decreased significantly upon opening the acoustic device, while agglomeration and de-agglomeration of raindrops were in a dynamic equilibrium. When the sound generator was on, the particle concentration at a sampling attitude of 500−1700 m and the proportion of particles with diameters of 1–1.5 mm decreased significantly (by 1–5 ln [1/m3·mm]). In contrast, the particle concentration increased by 1–3 ln [1/m3·mm] at a sampling attitude below 400 m. Moreover, during acoustic interference, the reflectivity factor surged by 2.71 dBZ within 1200 m of the operation centre. Overall, the spatial and temporal distributions of rainfall rates and cumulative precipitation within 5 km of acoustic operation were uneven and influenced by local terrain and background winds.

A Historical–Projected Analysis in Land Use/Land Cover in Developing Arid Region Using Spatial Differences and Its Relation to the Climate

Land resources are under relentless pressure from metropolitan regions, pollution, and climate shifts. The urge to monitor Land Use/Land Cover (LULC) and climate changes based on technology and sustainable management are addressed. This study analyzes the historical land cover maps to calculate growth patterns for the years 1985–2022 and uses Logistic Regression (LR) and Artificial Neural Networks (ANN) to project future dynamics forecasts for the years 2030–2040 in the Amman-Zarqa Basin (AZB). The state of the climate and the extreme indices projections of CMIP5 under RCP8.5 are linked to the corrected historical LULC maps and assessed. Given greater dry covering of large surface runoff, little rainfall, and high evapotranspiration rates, the state of the climate across the AZB notably showed instability in key climatic indices and a major exacerbation of warmth and drier soil in the basin. Both climate change and land use are contributing dynamics, but land-use alterations are much more dramatic changes than climate changes. Since the effects of climate alterations are mostly identifiable through land cover forms, land use practices put the phase that may be influenced by climate change. The results revealed that the daily extremes in 1992 are aligned with the corresponding increase of barren lands and diminished the half area of forest, cultivated, rainfed, and pasture lands in 1995. Rainfed regions were converted to agriculture or shrubland with an accuracy of 0.87, and urban encroachment caused the acreage of woodland, cultivated, rainfed, and grazing fields to decrease by almost half. Predicted land cover maps were created using LR in 2030 (Kappa = 0.99) and 2040 (Kappa = 0.90), in contrast to the ANN approach (Kappa = 0.99 for 2030 and 0.90 for 2040). By combining ANN and LR, decreasing bare soil was anticipated between 325 km2 and 344 km2. As a result, 20% of the total area of the major AZB cities’ urban areas will be doubled. More subjective analysis is required to study and predict drought in the future to improve the resilience of various LULC types.

Deep Spatial Interpolation of Rain Field for U.K. Satellite Networks

This article presents two new state-of-the-art spatial rain field interpolation convolutional neural networks (SRFICNNs), referred to as learned deviation (LD) and learned interpolation (LI) models, for predicting the point rain rate at finer spatial scales. The main contribution is the successful introduction of the prior-art deep learning technique into high-resolution (HR) rainfall rate prediction with significant improvement in accuracy. This is very important for the effective implementation of fade mitigation techniques for both terrestrial and satellite networks. The comparison of the models’ performances with ground truth (radar measurements) shows that the proposed models give an excellent mean square error (MSE) and structural SIMilarity (SSIM) in rainfall field reconstruction if the network depth falls in the range of 15–25 weight layers. The final model uses 20 layers for HR point rain rate prediction. Further study shows that the LD model offers a faster convergence and yields a more accurate rain rate prediction. In particular, this article compares the rain rate exceedance distribution and Log-Normality property from the model estimates with values calculated from measured data. Results show that the LD model gives a highly accurate estimate of these two indices with the corresponding root mean square (rms) error of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$5.1709\times 10^{-4}$ </tex-math></inline-formula> and 0.0013, respectively.

A Practical Approach for Determining Multi-Dimensional Spatial Rainfall Scaling Relations Using High-Resolution Time–Height Doppler Data from a Single Mobile Vertical Pointing Radar

The rescaling of rainfall requires measurements of rainfall rates over many dimensions. This paper develops one approach using 10 m vertical spatial observations of the Doppler spectra of falling rain every 10 s over intervals varying from 15 up to 41 min in two different locations and in two different years using two different micro-rain radars (MRR). The transformation of the temporal domain into spatial observations uses the Taylor “frozen” turbulence hypothesis to estimate an average advection speed over an entire observation interval. Thus, when no other advection estimates are possible, this paper offers a new approach for estimating the appropriate frozen turbulence advection speed by minimizing power spectral differences between the ensemble of purely spatial radial power spectra observed at all times in the vertical and those using the ensemble of temporal spectra at all heights to yield statistically reliable scaling relations. Thus, it is likely that MRR and other vertically pointing Doppler radars may often help to obviate the need for expensive and immobile large networks of instruments in order to determine such scaling relations but not the need of those radars for surveillance.

Efficiency of the NWC SAF Version 2021 CRRPh Precipitation Product: Comparison against Previous NWC SAF Precipitation Products and the Influence of Topography

The algorithm of the Convective Rainfall Rate with Microphysical Properties (CRRPh) product of the 2021 version of the Nowcasting and Very Short Range Forecasting Satellite Application Facility (NWC SAF) presents innovative characteristics. It was developed employing principal components analysis to reduce the number of utilized parameters and uses the same mathematical scheme for day and night, simulating the missing visual channels and satellite-derived cloud water path information that is unavailable during nighttime. Applying adequate statistical methodologies and scores and using rain gauge data as ground truth, it is shown that the new algorithm appears to be significantly improved compared to its predecessors in regard to the delineation of the precipitation areas. In addition, it minimizes the day–night difference in the estimation efficiency, which is a remarkable achievement. The new product suffers from slightly higher errors in the precipitation accumulations. Finally, it is shown that topography does not seem to affect the estimation efficiency of the product. In light of these results, it is argued that, overall, the new algorithm outperforms its predecessors and, possibly after adequate adaptations, can be used as a real-time total precipitation product.

Enhanced gravity wave activity in the mesosphere lower thermosphere region over Tirunelveli as a response to tropospheric convective event

The recent upgrade of Medium-Frequency (MF) wind radar at Tirunelveli (8.7⁰N, 77.8⁰E) has improved the height and time resolution of the wind measurements, which are utilized in the present study to examine high frequency (in periods 20–60 min) gravity waves (GW) in the mesosphere and lower thermosphere (MLT) region. We observe, in addition to the dominant wave activity during equinox months, dominant episodes of high-frequency GW activities in the meridional winds during the times 31 May–4 June and 25–27 June 2019. Using the perturbation ellipse method, we infer the direction of propagation of the GW and it is found to be in the north–south plane. The GW activity in the MLT region exhibits anti-correlation with NOAA outgoing longwave radiation (OLR) and positive correlation with rainfall rates indicating the latent heat release due to tropical convection as the possible source of the GW. Besides, the presence of dynamical instability is inferred from the calculations using the radar wind and the spaceborne SABER (Sounding of Atmosphere using Broadband Emission Radiometry) temperature data suggesting a possible causality of convectively generated GWs dissipation.

Creating a Risk Assessment Plan for Rainfall Impacts on Heritage Buildings Façades via Quantitative Methods

The unstable climate causes many related risks, including heavy rainfall. In the past, Egypt experienced rainfall over time. Although Egypt is still classified as a low-precipitation country, it can experience extreme rainfall events, resulting in flash floods. Many fragile materials related to heritage building façades may be affected by the change in rainfall rates without any risk assessment plan. This research aims to confront the hazards of raindrop strikes, as the need for making a risk management plan has arisen in order to preserve heritage buildings from heavy rainfall. The research methodology uses the ABC risk assessment quantitative method in addition to experts’ multi-round assessments and tornado diagrams to analyze the magnitude of risks in order to create a risk assessment plan for a group of heritage buildings in Historic Cairo as a pilot study that can be generalized for similar cases. The final output is framed into a cause-and-effect model for rainfall risk problem solving, generated by rainfall risk causes and effects on the heritage building facades.

Patterns and controlling factors of soil carbon sequestration in nitrogen-limited and -rich forests in China-a meta-analysis.

Soil organic carbon (SOC) management has the potential to contribute to climate change mitigation by reducing atmospheric carbon dioxide (CO2). Understanding the changes in forest nitrogen (N) deposition rates has important implications for C sequestration. We explored the effects of N enrichment on soil carbon sequestration in nitrogen-limited and nitrogen-rich Chinese forests and their controlling factors. Our findings reveal that N inputs enhanced net soil C sequestration by 5.52-18.46 kg C kg-1 N, with greater impacts in temperate forests (8.37-13.68 kg C kg-1 N), the use of NH4NO3 fertilizer (7.78 kg Ckg-1 N) at low N levels (<30 kg Ckg-1 N; 9.14 kg Ckg-1 N), and in a short period (<3 years; 12.95 kg C kg-1 N). The nitrogen use efficiency (NUE) varied between 0.24 and 13.3 (kg C kg-1 N) depending on the forest type and was significantly controlled by rainfall, fertilizer, and carbon-nitrogen ratio rates. Besides, N enrichment increased SOC concentration by an average of 7% and 2% for tropical and subtropical forests, respectively. Although soil carbon sequestration was higher in the topsoil compared to the subsoil, the relative influence indicated that nitrogen availability strongly impacts the SOC, followed by dissolved organic carbon concentration and mean annual precipitation. This study highlights the critical role of soil NUE processes in promoting soil C accumulation in a forest ecosystem.

Geophysical and Remote Sensing Assessment of Chad’s Groundwater Resources

Because of climate change and human activity, North and Central Africa are experiencing a significant water shortage. Recent advancements in earth observation technologies have made widespread groundwater monitoring possible. To examine spatial and temporal mass fluctuations caused by groundwater variations in Chad, gravity solutions from the Gravity Recovery and Climate Experiment (GRACE), climatic model outputs, and precipitation data are integrated. The results are as follows: (1) The investigated region experienced average annual precipitation (AAP) rates of 351.6, 336.22, and 377.8 mm yr−1, throughout the overall investigation period (04/2002–12/2021), Period I (04/2002–12/2011), and Period II (01/2012–12/2021), respectively. (2) Using the three gravity solutions, the average Terrestrial Water Storage Variations (ΔTWS) values are estimated to be +0.26 ± 0.04, +0.006 ± 0.10, and +0.64 ± 0.12 cm yr−1, for the overall study period, periods I, and II, respectively. (3) Throughout the full period, periods I, and II, the groundwater storage fluctuations (ΔGWS) are calculated to be +0.25 ± 0.04, +0.0001 ± 0.099, and +0.62 ± 0.12 cm yr−1, respectively after removing the soil moisture (ΔSMS) and Lake Chad water level trend values. (4) The country receives an average natural recharge rate of +0.32 ± 0.04, +0.068 ± 0.099, and +0.69 ± 0.12 cm yr−1, throughout the whole period, Periods I, and II, respectively. (5) The southern mountainous regions of Erdi, Ennedi, Tibesti, and Darfur are receiving higher rainfall rates that may recharge the northern part of Chad through the stream networks; in addition to the Lake Chad and the higher rainfall over southern Chad might help recharge the central and southern parts of the country. (6) A preferred groundwater flow path from the Kufra (Chad and Libya) to the Dakhla basin (Egypt) appears to be the Pelusium mega shear system, which trends north-east. The findings suggest that GRACE is useful for monitoring changes in groundwater storage and recharge rates across large areas. Our observation-based methodology provides a unique understanding of monthly ground-water patterns at the state level, which is essential for successful interstate resource allocation, future development, and policy initiatives, as well as having broad scientific implications for arid and semiarid countries.

Data assimilation, sensitivity analysis and uncertainty quantification in semi-arid terminal catchments subject to long-term rainfall decline

Quantification of long-term hydrologic change in groundwater often requires the comparison of states pre- and post-change. The assessment of these changes in ungauged catchments using numerical models and other quantitative methods is particularly difficult from a conceptual point of view and due to parameter non-uniqueness and associated uncertainty of quantitative frameworks. In these contexts, the use of data assimilation, sensitivity analysis and uncertainty quantification techniques are critical to maximize the use of available data both in terms of conceptualization and quantification. This paper summarizes findings of a study undertaken in the Lake Muir-Unicup Natural Diversity Recovery Catchment (MUNDRC), a small-scale endorheic basin located in southwestern Australia that has been subject to a systematic decline in rainfall rates since 1970s. A combination of data assimilation techniques was applied to conceptual and numerical frameworks in order to understand and quantify impacts of rainfall decline on the catchment using a variety of metrics involving groundwater and lake levels, as well as fluxes between these compartments and mass balance components. Conceptualization was facilitated with the use of a novel data-driven method relating rainfall and groundwater responses running backwards in time, allowing the establishment of the likely baseline conditions prior to rainfall decline, estimation of net recharge rates and providing initial heads for the forward numerical modelling. Numerical model parameter and predictive uncertainties associated with data gaps were then minimized and quantified utilizing an Iterative Ensemble Smoother algorithm, while further refinement of conceptual model was made possible following results from sensitivity analysis, where major parameter controls on groundwater levels and other predictions of interest were quantified. The combination of methods can be considered as a template for other long-term catchment modelling studies that seek to constrain uncertainty in situations with sparse data availability.

Study on the Quantitative Precipitation Estimation of X-Band Dual-Polarization Phased Array Radar from Specific Differential Phase

In this study, the quantitative precipitation estimation (QPE) capability of three X-band dual-polarization phased array radars (PAR) in Guangzhou, South China, was demonstrated, with an S-band operational dual-polarization radar as the benchmark. Rainfall rate (R) estimators based on the specific differential phase (KDP) for summer precipitation for both X-band and S-band radars were derived from the raindrop size distributions (DSDs) observed by a 2-dimensional video disdrometer (2DVD) in South China. Rainfall estimates from the radars were evaluated with gauge observations in three events, including pre-summer rainfall, typhoon precipitation, and local severe convective precipitation. Observational results showed that radar echoes from the X-band PARs suffered much more severely from attenuation than those from the S-band radar. Compared to S-band observations, the X-band echoes can disappear when the signal-to-noise ratio drops to a certain level due to severe attenuation, resulting in different estimated rainfall areas for X- and S-band radars. The attenuation corrected by KDP had good consistency with S-band observations, but the accuracy of attenuation correction was affected by DSD uncertainty and may vary in different types of precipitation. The QPE results demonstrated that the R(KDP) estimator produced better rainfall accumulations from the X-band PAR observations compared to the S-band observations. For both the X-band and S-band radars, the estimates of hourly accumulated rainfall became more accurate in heavier rainfall, due to the decreases of both the DSD uncertainty and the impact of measurement errors. In the heavy precipitation area, the estimation accuracy of the X-band radar was high, and the overestimation of the S-band radar was obvious. Through the analysis of the ZH-ZDR distribution in the three weather events, it was found that the X-band PAR with the capability of high spatiotemporal observations can capture minute-level changes in the microphysical characteristics, which help improve the estimation accuracy of ground rainfall.

Evaluation of WRF Simulation of Deep Convection in the U.S. Southern Great Plains

Abstract The Southern Great Plains (SGP) region exhibits a relatively high frequency of periods with extremely high rainfall rates (RR) and hail. Seven months of 2017 are simulated using the Weather Research and Forecasting (WRF) Model applied at convection-permitting resolution with the Milbrandt–Yau microphysics scheme. Simulation fidelity is evaluated, particularly during intense convective events, using data from ASOS stations, dual-polarization radar, and gridded datasets and observations at the DOE Atmospheric Radiation Measurement site. The spatial gradients and temporal variability of precipitation and the cumulative density functions for both RR and wind speeds exhibit fidelity. Odds ratios &gt; 1 indicate that WRF is also skillful in simulating high composite reflectivity (cREF, used as a measure of widespread convection) and RR &gt; 5 mm h−1 over the domain. Detailed analyses of the 10 days with highest spatial coverage of cREF &gt; 30 dBZ show spatially similar reflectivity fields and high RR in both radar data and WRF simulations. However, during periods of high reflectivity, WRF exhibits a positive bias in terms of very high RR (&gt;25 mm h−1) and hail occurrence, and during the summer and transition months, maximum hail size is underestimated. For some renewable energy applications, fidelity is required with respect to the joint probabilities of wind speed and RR and/or hail. While partial fidelity is achieved for the marginal probabilities, performance during events of critical importance to these energy applications is currently not sufficient. Further research into optimal WRF configurations in support of potential damage quantification for these applications is warranted. Significance Statement Heavy rainfall and hail during convective events are challenging for numerical models to simulate in both space and time. For some applications, such as to estimate damage to wind turbine blades and solar panels, fidelity is also required with respect to hail size and joint probabilities of wind speed and hydrometeor type and rainfall rates (RR). This demands fidelity that is seldom evaluated. We show that, although this simulation exhibits fidelity for the marginal probabilities of wind speed, RR, and hail occurrence, the joint probabilities of these properties and the simulation of maximum size of hail are, as yet, not sufficient to characterize potential damage to these renewable energy industries.

Analysis of sediment load on meandering channel due to combined effect of rainfall and flow

Topography, geology, climate change and anthropogenic activity are key factors for erosion that affects channel morphology. Water erosion leads to sedimentation. The study of sediment is important for the management of water quality, river structures as well as flood control. Erosion of soil by water poses a serious problem in terms of degradation of land and eutrophication of water. River or stream erosion is mainly caused by rainfall and streamflow as they detach soil particles and transport them as sediment. The meandering channel is common in most rivers or streams where it is formed by the deposition of sediments due to the depletion of water energy. Therefore, this study aims to analyse the sediment load under the combined effect of rainfall and flow for a meandering channel. This study was conducted based on the Advanced Environmental Hydrology System to determine the relationship among sediment load, rainfall and flow rate. Twenty-five sets of experiments were conducted on a 2 m long, 20 cm wide and 4 cm deep model meandering channel, moulded in the Armfield S12 MKII on a 1% constant slope. Five different rainfalls intensities (0.46 mm/hr, 1.39 mm/hr, 2.78 mm/hr, 3.70 mm/hr and 4.63 mm/hr) and five different flow rates (0.5 L/min, 1.0 L/min, 1.5 L/min, 1.75 L/min and 2.0 L/min) were used to analyse the different trends of sediment load with respect to rainfall and flow rate. This study shows that there is a correlation among rainfall, flow and sediment load. The erosion rate during lowest flow and rainfall is 4.75 g/min and for highest rainfall and flow is 70.2 g/min.

Hypothetical Ground Radar-Like Rain Rate Generation of Geostationary Weather Satellite Using Data-to-Data Translation

This study proposes a deep-learning-based data-to-data (D2D) translation framework to simulate a radar-like retrieval of rainfall rates using the advantages of spatial coverage and temporal resolution of geostationary (GEO) satellite observation. The D2D method comprises normalization and denormalization in pre- and post-processing and an adversarial learning structure for an inter-domain conversion between physical values of data such as albedo and brightness temperature (BT) unlike the image-to-image translation using digital number values in image data. The GEO-KOMPSAT-2A (GK2A) and radar hybrid surface rainfall (HSR) datasets over the Korean Peninsula from September 2019 to September 2021 were used as the source and target domains for training and testing the D2D model. The constructed D2D model for ground radar-like rainfall generation was validated using the ground radar-observed rainfall data and compared to the GK2A rainfall rate (RR), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS), and Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) rainfall products. The D2D model exhibited excellent performance for various rain types in the study area compared to the GK2A RR, PERSIANN-CCS, and IMERG data. Consequently, the D2D model can provide valuable and accurate radar-like rainfall intensity and distribution data with a high temporal resolution and complementary rainfall information over lands and oceans without radar observation.

Determining Variables Associated with Annual Oil Palm Yield: An Explainable Gradient Boosting Approach

With the growing demands for Precision Agriculture (PA) in Indonesia, researchers have evaluated the utilization of Machine Learning for predicting oil palm yields and determining variables affecting them. Previous studies showed that meteorological variables, including rainfalls and wind speed, are associated with oil palm yields. In this research, the Extreme Gradient Boosting (XGBoost) model and the Shapley Additive exPlanations (SHAP) were deployed for analyzing the importance of 15 agrometeorological variables in predicting oil palm yield. The best model attained 1.911 RMSE and 0.855 R2. By analyzing the weights and gains of the XGBoost model along with the SHAP values, it was found that the yield in the previous year, the age and number of plants, the area of peat lands, and meteorological parameters such as rainfall rates and the number of rainy days in the previous three years were considered important. The previous year's yield in particular possesses the greatest weight and gain values according to the model, and the highest SHAP value among all input variables. However, the meteorological data used in this research are only limited to rainfall rates and the number of rainy days. In the future, more diverse variables can be analyzed.

Rainfall Rate and Water Quality of Boreholes and Wells in the District of Akassato in the Municipality of Abomey-calavi in Benin, West Africa

Rainfall Rate and Water Quality of Boreholes and Wells in the District of Akassato in the Municipality of Abomey-calavi in Benin, West Africa

Orographic Controls over Convection in an Inter-Andean Valley in Northern South America

Abstract Convective processes are critical in the northernmost section of the Andes, where highly complex orography combines with tropical dynamics to produce intense precipitating systems. Data from satellite and ground radar have shown that Colombia’s central Magdalena valley has a marked precipitation diurnal cycle, with high rainfall rates produced by deep convective events that intensify on the foothills of the surrounding valley. This study aims to contribute to the understanding of the orographic controls imposed on the convective activity observed in this important tropical valley. The atmospheric environments associated with convective events are assessed using WRF high-resolution simulations for a two-week period that includes intense rainfall events and marked dry spells. In addition, a simulation with modified orography is evaluated to analyze further the effect of topography in controlling the observed convective systems. The results suggest that northward and southward wind regimes channeled by the valley’s topography and cross-valley flow result in a robust nocturnal convergence zone, which is associated with the strong diurnal cycle and the valley configuration. Furthermore, northerly low-level flow is an important source of moisture for convection in the valley. Additionally, results show midlevel tropospheric heating enhancement during days with more rainfall produced by convective systems in the western slope of the valley. The modified topography simulation suggests that circulation and precipitation in the valley are strongly dependent on the actual terrain features.

Fenologia reprodutiva de Passiflora cincinnata Mast. na região semiárida do Estado da Bahia, Brasil, com base em dados de herbário e interpolação pelo inverso da distância - IDW

ABSTRACT We investigated intensity and seasonality of the reproductive phenology of Passiflora cincinnata Mast. and estimated its flowering and fruiting periods by interpolation using Inverse Distance Weighting, for the first time in phenology, based on herbarium collections from Bahia State, Northeastern Brazil. Data from 249 exsiccates with flowers and/or fruits were analyzed to estimate the amplitude of flowering and fruiting and test the seasonality using the circular statistics. Passiflora cincinnata flowering and fruiting was greatest from October to May. The maps generated by interpolation showed greatest probability of flowering and fruiting between March and May in sites with mean annual rainfall rates between 500 and 1000 mm. Our results indicated seasonality of flowering and fruiting, with reduced intensities during the austral winter. The interpolation methodology can be used in a complementary or preliminary way, either to organize field visits and set up experiments, or to extrapolate field results for larger-scale views.

Assessing the impact of a NWP warm‐start system on model spin‐up over tropical Africa

AbstractConvection‐permitting (CP) models have provided a step change in the ability to forecast impactful convective storms, which pose risks such as flash flooding and lightning. Despite CP models being routinely run over tropical Africa, they typically lack data assimilation and are initialised directly from a global analysis. The CP model therefore takes time to generate convective structures and consequently precipitation, rendering the early part of the forecast unusable. A ‘warm‐starting’ method developed within the Met Office Unified Model is tested over a large tropical Africa domain, leading to substantial improvements in rainfall predictions during both the spin‐up period but also later in the forecast. The spin‐up period is found to vary considerably, both geographically and with model initialisation time, due to the differing states of the atmosphere through the day. We quantify this impact through a spin‐up diagnostic, which shows that over central Africa the spin‐up period can be as large as 24 hr. In these regions, the warm starting substantially reduces spin‐up time. Additionally, the large tropical Africa domain allows us to examine the impact of the prevailing meteorology on both the spin‐up period and the warm‐start method. We find large‐scale positive vorticity filaments over West Africa, likely tied to African Easterly Waves, increase rainfall rates across all models. The substantial reduction in the spin‐up of rainfall and increased skill at longer lead times in the warm‐start demonstrate the importance of representing convective features in the analysis for a CP model. This is especially important in large tropical domains where the initial conditions are likely to have a lasting impact on the forecast. Improvements in model performance across the forecast will enhance the ability of forecasters to provide advice on high‐impact weather events, supporting increased resilience against weather extremes in Africa.

Physical responses of Baiu extreme precipitation to future warming: Examples of the 2018 and 2020 western Japan events

Baiu (Meiyu) is a continuous and large-scale rainfall phenomenon in East Asia, whose extreme events have caused many disasters in recent years. Global warming will accelerate the global water cycle and tend to increase the risks of extreme precipitation events. However, to date, the response of Baiu extreme rainfall to future climate warming is still not well investigated. In this study, by conducting the ensemble experiments with a very high resolution (2 km) regional climate model and pseudo warming technique, we reproduced two Baiu extreme rainfall events in 2018 and 2020 that caused severe losses in western Japan, and investigated its response to a +4K warmer climate. We found that climate warming will apparently increase the intensity of the peak Baiu precipitation, and the increasing rates can be much larger than the well-known Clausius-Clapeyron (C–C) scaling (∼7% K−1) with the maximum rate reaching 16.7% K−1 for Shikoku in the 2018 event. This increase in peak precipitation is jointly caused by the increased atmospheric moisture and the accelerated vertical air motion. However, both super and under C–C scaling of changes are expected for total precipitation, with the range from 1.5% K−1 to 17.4% K−1 for different regions and events. The rainfall change rates are jointly influenced by moisture transport direction and elevation, topography, and vertical air motion. The moisture inflow with lower elevation tends to cause a larger rainfall increase. It should also be noted that our regional simulations underestimate the extreme precipitation when reproducing the two events, which is caused by the bias in the GCM data. Implementing bias correction methods or future improvement of GCM simulations could be the solutions for future study. Overall, this study points out the risk of extreme Baiu rainfall under future global warming, and highlights the different increasing rates of extreme Baiu rainfall due to specific climate conditions.