Precipitation Observations Research Articles

A deep learning model for reconstructing centenary water storage changes in the Yangtze River Basin

Since 2002, the Gravity Recovery and Climate Experiment (GRACE) and its Follow-On mission (GRACE-FO) have facilitated highly accurate observations of changes in total water storage anomalies (TWSA). However, limited observations of TWSA derived from GRACE in the Yangtze River Basin (YRB) have hindered our understanding of its long-term variability. In this paper, we present a deep learning model called RecNet to reconstruct the climate-driven TWSA in the YRB from 1923 to 2022. The RecNet model is trained on precipitation, temperature, and GRACE observations with a weighted mean square error (WMSE) loss function. The performance of the RecNet model is validated and compared against GRACE data, water budget estimates, hydrological models, drought indices, and existing reconstruction datasets. The results indicate that the RecNet model can successfully reconstruct historical water storage changes, surpassing the performance of previous studies. In addition, the reconstructed datasets are utilized to assess the frequency of extreme hydrological conditions and their teleconnections with major climate patterns, including the El Niño-Southern Oscillation, Indian Ocean Dipole, Pacific Decadal Oscillation, and North Atlantic Oscillation. Independent component analysis is employed to investigate individual climate patterns' unique or combined influence on TWSA. We show that the YRB exhibits a notable vulnerability to extreme events, characterized by a recurrent occurrence of diverse extreme dry/wet conditions throughout the past century. Wavelet coherence analysis reveals significant coherence between the climate patterns and TWSA across the entire basin. The reconstructed datasets provide valuable information for studying long-term climate variability and projecting future droughts and floods in the YRB, which can inform effective water resource management and climate change adaptation strategies.

A 20-year satellite-reanalysis-based climatology of extreme precipitation characteristics over the Sinai Peninsula

Abstract. Extreme precipitation events and associated flash floods caused by synoptic cyclonic systems profoundly impact society and the environment, particularly in arid regions. This study brings forward a satellite-reanalysis-based approach to quantify extreme precipitation characteristics over the Sinai Peninsula (SiP) in Egypt from a statistical–synoptic perspective for the period of 2001–2020. With a multi-statistical approach developed in this research, SiP's wet and dry periods are determined. Using satellite observations of precipitation and a set of derived precipitation indices, we characterize the spatiotemporal variations of extreme rainfall climatologies across the SiP. Then, using the reanalysis datasets, synoptic systems responsible for the occurrence of extreme precipitation events along with the major tracks of cyclones during the wet and dry periods are described. Our results indicate that trends and spatial patterns of the rainfall events across the region are inconsistent in time and space. The highest precipitation percentiles (∼20 mm per month), frequencies (∼15 d per month with rainfall ≥10 mm d−1), standard deviations (∼9 mm month per month), and monthly ratios (∼18 %) are estimated in the northern and northeastern parts of the region during the wet period, especially in early winter; also, a substantial below-average precipitation condition (drier trend) is clearly observed in most parts except for the south. Mediterranean cyclones accompanied by the Red Sea and Persian troughs are responsible for the majority of extreme rainfall events year-round. A remarkable spatial relationship is found between SiP's rainfall and the atmospheric variables of sea level pressure, wind direction, and vertical velocity. A cyclone-tracking analysis indicates that 125 cyclones (with rainfall ≥10 mm d−1) formed within, or transferred to, the Mediterranean basin and precipitated over the SiP during wet periods compared to 31 such cyclones during dry periods. It is estimated around 15 % of cyclones with sufficient rainfall >40 mm d−1 would be capable of leading to flash floods during the wet period. This study, therefore, sheds new light on the extreme precipitation characteristics over the SiP and its association with dominant synoptic-scale mechanisms over the eastern Mediterranean region.

Meteorological and hydrological drought analysis of Sinop, Kastamonu, Bartın provinces in the Western Black Sea

In recent years, a significant change has been observed in the climate characteristics of Turkey. These changes, which are compatible with the general trend of global climate change, are also felt in the Western Black Sea Basin. This paper reported on the calculation of drought indices, with emphasis on two recently developed indices, the Reconnaissance Drought Index (RDI) and the Stream flow Drought Index (SDI) using specialized software package, named Drin C (Drought Indices Calculator). Additionally, Drin C includes a module for the estimation of potential evapo transpiration (PET) through temperature-based methods (Hargreaves, Blaney - Criddle, and Thornthwaite) that can be used for the calculation of RDI. Therefore, in this study carried out in the Western Black Sea region, meteorological drought analyzes for1-, 3-, 6- ve 12 months were made for 3 precipitation observation stations in the provinces of Sinop, Kastamonu, Bartın, and monthly total precipitation and monthly average temperature data were used to determine the meteorological drought.

Characteristics of mean and extreme precipitation in Ny Ålesund, Arctic

The intensification of precipitation in the Arctic can impact the ecosystem by altering the freshwater flux, albedo, sea ice, and permafrost stability. In a location like the Arctic with a harsh environment, precipitation observations are difficult to make. In the present study, precipitation characteristics are investigated using precipitation records from Ny Ålesund, Svalbard, where the longest record of precipitation in the Atlantic sector of the Arctic is available. Precipitation is expected to increase with the warming of the atmosphere; however, significant trends in precipitation are only observed during fall (0.36 mm/decade). Even though a strong temperature increase was noticed in winter, the precipitation trend was not significant. Precipitation is separated into weak and extreme events to gain more insight into its characteristics. Again, fall was characterised by a significant positive trend in weak precipitation (150 × 10−3mm/decade), while extreme winter precipitation displayed a positive trend during 2000–2019 (4.13 mm/decade). Further analysis indicated that winter extreme precipitation followed a 2-CC relationship while the precipitation during fall followed 2-CC/1-CC scaling for mean/extreme events. The atmospheric circulation pattern during the extreme events resembles the Scandinavian pattern, characterised by high MSLP anomalies over northwestern Europe. While widespread evaporation was noticed over the North Atlantic during extreme events, vertically integrated moisture divergence indicated West Spitzbergen Current (WSC) could be a possible moisture source for extreme precipitation events during winter.

The emergence of a climate change signal in long-term Irish meteorological observations

Detecting the emergence of a forced anthropogenic climate change signal from observations is critical for informing adaptation responses. By regressing local variations in climate onto annual Global Mean Surface Temperature (GMST), we track the emergence of an anthropogenic signal in long-term quality assured observations of temperature and precipitation for the island of Ireland, a sentinel location on the western European Atlantic seaboard. Analysis of station based observations, together with island scale composite series is undertaken for annual and seasonal means, together with 16 indices of extremes, with the derived signal-to-noise ratio classified as normal, unusual or unfamiliar relative to early industrial climate. More than half of indices show the emergence of at least unusual conditions relative to early industrial climate. The increase in annual mean temperature has led to the emergence of unfamiliar climate at six of eleven stations. Warming at the island scale is estimated at 0.88 °C per degree warming in GMST. While many stations show the emergence of unusual climate for spring, summer and autumn mean temperature, no forced signal of change is found for winter mean temperature. Changes in cool/warm days and nights are unfamiliar relative to early industrial climate. However, no anthropogenic signal is found for the hottest day annually or in summer – an extreme often associated with climate change in public consciousness. Increases in annual precipitation totals have emerged as unusual for western stations with large increases in winter totals per degree warming in GMST (e.g., 25.2% and 19.7% at Malin Head and Markree, respectively), indicating heightened flood risk with continued warming. By contrast, summer precipitation shows no significant relationship with GMST. Increases in rainfall intensity have emerged as unusual for 30% of stations, with increases consistent with the Clausius-Clapeyron relationship. Our analysis shows that an emerging climate change signal is discernible for Ireland, a location strongly influenced by climate variability.

Analysis of the Characteristics of Uneven Spatio-Temporal Distribution in Wujiang River Basin over the Last 60 Years

Exploring the characteristics of uneven temporal and spatial distribution of precipitation in mountain watersheds can provide a reference for regional agricultural development and resource utilization, and contribute to the protection of the ecological environment. Based on the daily precipitation observation data of 40 meteorological stations in the Wujiang River Basin from 1963 to 2021, the temporal- and spatial-variation characteristics of the precipitation concentration degree (PCD) and precipitation concentration period (PCP) were analyzed using the Randall S analysis method, Mann–Kendall test method, Pettitt method, wavelet analysis and empirical orthogonal function (EOF). The results showed that the fluctuation range of PCD in Wujiang River Basin from 1963 to 2021 was 0.34–0.59, with a multi-year average of 0.47, which was obviously higher than the national average level and is showing a trend of slowly increasing. The fluctuation range of PCP was between 17.1 and 21.5 days, with a multi-year average of 19.0. The annual precipitation was mostly concentrated around the middle of July and showed a slowly decreasing trend. The abrupt change in PCD and PCP occurred around 1983 and 2001, respectively. There is an obvious Hearst phenomenon in PCP. In the future, the trend of precipitation concentration in the middle period will remain in advance, and the degree of precipitation concentration will continue to increase. The maximum precipitation in the flood season will continue to be delayed. The spatial pattern of the first mode of PCD and PCP in the Wujiang River basin was consistent and showed an opposite pattern between the upper reaches and the middle-lower reaches of the basin, which reflects the influence of the topography of the basin on the spatial distribution of precipitation. The distribution of precipitation is affected by topography. The elevation change in the basin was complex, and the leeward slope varied a lot. Therefore, it has a significant impact on precipitation. Areas with less precipitation are at higher elevations and on mountain leeward slopes, with a lack of moist air flow. The area with more precipitation was the summer monsoon mountain windward slope, and the topography blocks the increase in precipitation. The elevation of the central region is relatively uniform and the terrain is flat. Therefore, the distribution of precipitation is more uniform.

Projections of Mean and Extreme Precipitation Using the CMIP6 Model: A Study of the Yangtze River Basin in China

In this study, we conducted an analysis of the CN05.1 daily precipitation observation dataset spanning from 1985 to 2014. Subsequently, we ranked the 30 global climate model datasets within the NEX-GDDP-CMIP6 dataset using the RS rank score method. Multi-model weighted-ensemble averaging was then performed based on these RS scores, followed by a revision of the multi-model weighted-ensemble averaging (rs-MME) using the quantile mapping method. The revised rs-MME model data were utilized for simulating precipitation variations within the Yangtze River Basin. We specifically selected 11 extreme-precipitation indices to comprehensively evaluate the capability of the revised rs-MME model data in simulating extreme-precipitation occurrences in the region. Our investigation culminated in predicting the characteristics of precipitation and the potential shifts in extreme-precipitation patterns across the region under three distinct shared socioeconomic pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5) for three temporal segments: the Near 21C (2021–2040), Mid 21C (2041–2070), and Late 21C (2071–2100). Our findings reveal that the revised rs-MME model data effectively resolve the issues of the overestimation and underestimation of precipitation data present in the previous model. This leads to an enhanced simulation of mean annual precipitation, the 95th percentile of precipitation, and the extreme-precipitation index for the historical period. However, there are shortcomings in the simulation of linear trends in mean annual precipitation, alongside a significant overestimation of the CWD and CDD indices. Furthermore, our analysis forecasts a noteworthy increase in future mean annual precipitation within the Yangtze River Basin region, with a proportional rise in forced radiation across varying scenarios. Notably, an ascending trend of precipitation is detected at the headwaters of the Yangtze River Basin, specifically under the Late 21C SSP5-8.5 scenario, while a descending trend is observed in other scenarios. Conversely, there is an escalating pattern of precipitation within the middle and lower reaches of the Yangtze River Basin, with most higher-rate changes situated in the middle reaches. Regarding extreme-precipitation indices, similar to the annual average precipitation, a remarkable upsurge is evident in the middle and lower reaches of the Yangtze River Basin, whereas a relatively modest increasing trend prevails at the headwaters of the Yangtze River Basin. Notably, the SSP5-8.5 scenario portrays a substantial increase in extreme-precipitation indices.

A set of methods to evaluate the below-cloud evaporation effect on local precipitation isotopic composition: a case study for Xi'an, China

Abstract. When hydrometeors fall from an in-cloud saturated environment toward the ground, especially in arid and semiarid regions, below-cloud processes may heavily alter the isotopic composition of precipitation through equilibrium and non-equilibrium fractionations. If these below-cloud processes are not correctly identified, they can lead to misinterpretation of the precipitation isotopic signal. To correctly understand the environmental information recorded in the precipitation isotopes, qualitatively analyzing the below-cloud processes and quantitatively calculating the below-cloud evaporation effect are two important steps. Here, based on 2 years of synchronous observations of precipitation and water vapor isotopes in Xi'an, China, we compiled a set of effective methods to systematically evaluate the below-cloud evaporation effect on local precipitation isotopic composition. The ΔdΔδ diagram is a tool to effectively diagnose below-cloud processes, such as equilibration or evaporation, because the isotopic differences (δ2H; d-excess) between the precipitation-equilibrated vapor and the observed vapor show different pathways. By using the ΔdΔδ diagram, our data show that evaporation is the major below-cloud process in Xi'an, while snowfall samples retain the initial cloud signal because they are less impacted by the isotopic exchange between vapor and solid phases. Then, we chose two methods to quantitatively characterize the influence of below-cloud evaporation on local precipitation isotopic composition. One is based on the raindrop's mass change during its falling (hereafter referred to as method 1), and the other is dependent on the variations in precipitation isotopic composition from the cloud base to the ground (hereafter referred to as method 2). By comparison, we found that there are no significant differences between the two methods in evaluating the evaporation effect on δ2Hp, except for snowfall events. The slope of the evaporation in proportion to the variation in δ2H (Fi/Δδ2H) is slightly larger in method 1 (1.0 ‰ %−1) than in method 2 (0.9 ‰ %−1). Additionally, both methods indicate that the evaporation effect is weak in autumn and heavy in spring. Through a sensitivity test, we found that in two methods, relative humidity is the most sensitive parameter, while the temperature shows different effects on the two methods. Therefore, we concluded that both methods are suited to the investigation of the below-cloud evaporation effect, while in method 2, other below-cloud processes, such as supersaturation, can still be included. By applying method 2, the diagnosis of below-cloud processes and the understanding of their effects on the precipitation isotopic composition will be improved.

Cloud and Precipitation in Low-Latitude Extratropical Cyclones Conditionally Sorted on CYGNSS Surface Latent and Sensible Heat Fluxes

Abstract Extratropical cyclones are the primary cause of midlatitude winds, precipitation, and clouds. Surface latent and sensible heat fluxes from the ocean impact cyclone intensity, but their role in moisture transport leading to cloud and precipitation is still under investigation. While numerical simulations can help establish such links, evaluating and constraining these simulations require observations. To this end, satellite-based observations of cloud and precipitation in low-latitude extratropical cyclones are examined for four distinct classifications based on the Cyclone Global Navigation Satellite System (CYGNSS) latent and sensible heat fluxes, averaged in the post-cold-frontal or warm-sector areas of the cyclones. Using a cyclone compositing approach, contrasts in cloud and precipitation in strong- versus weak-surface-flux conditions are examined. In the post-cold-frontal region, stronger latent or sensible heat fluxes are associated with lower precipitation rates and higher cloud opacity, indicating more vigorous shallow convection. However, larger sensible heat flux cases display larger cloud fraction, while larger latent heat flux cases exhibit lower cloud fraction, which could indicate differing cloud morphologies. In the comma region of the cyclones, clouds and precipitation depend on both cyclone strength and moisture availability. Consistent with this, larger cloud amount and precipitation are found for strong fluxes in the post-cold-frontal region, and weak or negative sensible heat fluxes, indicative of poleward warm advection, in the warm sector. The strong regional differences in the surface heat flux–cloud and precipitation relationships highlight the need for further investigation into moisture supply and transport in cyclones, while providing guidance for future work.

Utility of Open-Access Long-Term Precipitation Data Products for Correcting Climate Model Projection in South China

Insufficient precipitation observations hinder the bias-correction of Global Climate Model (GCM) precipitation outputs in ungauged and remote areas. As a result, the reliability of future precipitation and water resource projections is restricted for these areas. Open-access quantitative precipitation estimation (QPE) products offer a potential solution to this challenge. This study assesses the effectiveness of three widely used, long-term QPEs, including ERA5, PERSIANN-CDR, and CHIRPS, in bias-correcting precipitation outputs from the CMIP6 GCMs. The evaluation involves the reproduction of precipitation distribution, streamflow simulation utility based on a hydrological model, and the accuracy of extreme indices associated with rainstorm/flood/drought events. This study selects the Beijiang basin located in the subtropical monsoon area of South China as the case study area. The results demonstrate that bias-correction using QPEs improves the performance of GCM precipitation outputs in reproducing precipitation/streamflow distribution and extreme indices, with a few exceptions. PCDR generally exhibits the most effective bias-correction utility, consistently delivering reasonable performance across various cases, making it a suitable alternative to gauge data for bias-correction in ungauged areas. However, GCM outputs corrected by ERA5 tend to overestimate overall precipitation and streamflow (by up to about 25% to 30%), while the correction results of CHIRPS significantly overestimate certain extreme indices (by up to about 50% to 100%). Based on the revealed performance of QPEs in correcting GCM outputs, this study provides references for selecting QPEs in GCM-based water resource projections in remote and ungauged areas.

Runoff and Sediment Yield Processes in a Tropical Eastern Indian River Basin: A Multiple Machine Learning Approach

Tropical Indian river basins are well-known for high and low discharges with high peaks of flood during the summer and the rest of the year, respectively. A high intensity of rainfall due to cyclonic and monsoon winds have caused the tropical Indian rivers to witness more runoff. These rivers are also known for carrying a significant amount of sediment load. The complex and non-linear nature of the sediment yield and runoff processes and the variability of these processes depend on precipitation patterns and river basin characteristics. There are a number of other elements that make it difficult to forecast with great precision. The present study attempts to model rainfall–runoff–sediment yield with the help of five machine learning (ML) algorithms—support vector regression (SVR), artificial neural network (ANN) with Elman network, artificial neural network with multilayer perceptron network, adaptive neuro-fuzzy inference system (ANFIS), and local linear regression, which are useful in river basins with scarce hydrological data. Daily, weekly, and monthly runoff and sediment yield (SY) time series of Vamsadhara river basin, India for a period from 1 June to 31 October for the years 1984 to 1995 were simulated using models based on these multiple machine learning algorithms. Simulated results were tested and compared by means of three evaluation criteria, namely Pearson correlation coefficient, Nash–Sutcliffe efficiency, and the difference of slope. The results suggested that daily and weekly predictions of runoff based on all the models can be successfully employed together with precipitation observations to predict future sediment yield in the study basin. The models prepared in the present study can be helpful in providing essential insight to the erosion–deposition dynamics of the river basin.

On the Collective Importance of Model Physics and Data Assimilation on Mesoscale Convective System and Precipitation Forecasts over Complex Terrain

Abstract Forecasting mesoscale convective systems (MCSs) and precipitation over complex terrain is an ongoing challenge even for convective-permitting numerical models. Here, we show the value of combining mesoscale constraints to improve short-term MCS forecasts for two events during the North American monsoon season in 2013, including the following: 1) the initial specification of moisture, via GPS-precipitable water vapor (PWV) data assimilation (DA); 2) kinematics via modification of cumulus parameterization; and 3) microphysics via modification of cloud microphysics parameterization. A total of five convective-permitting Weather Research and Forecasting (WRF) Model experiments is conducted for each event to elucidate the impact of these constraints. Results show that combining GPS-PWV DA with a modified Kain–Fritsch scheme and double-moment microphysics provides relatively the best forecast of both North American monsoon MCSs and convective precipitation in terms of timing, location, and intensity relative to available precipitation and cloud-top temperature observations. Additional examination on the associated reflectivity, vertical wind field, equivalent potential temperature, and hydrometeor distribution of MCS events show the added value of each individual constraint to forecast performance. Significance Statement Forecasting thunderstorm clouds and rain over mountainous regions is challenging because of limitations in having radar and rain gauges and in resolving physical drivers in forecast models. We examine the value of considering all possible constraints by incorporating moisture into these models, and correcting physics in the model treatment of cumulus and cloud microphysics parameterizations. This study demonstrates that assimilating moisture and using modified Kain–Fritsch and double-moment microphysics schemes provides the best thunderstorm cloud and rain forecasts in terms of timing, location, and intensity. Each correction improves key properties of these storms such as vertical wind, along with distribution of water in various phases. We highlight the need to improve our efforts on effectively integrating these constraints into current and future forecasts.

Joint Collaboration on Comparing NOAA’s Ground-Based Weather Radar and NASA–JAXA’s Spaceborne Radar

Abstract The National Aeronautics and Space Administration (NASA) and National Oceanic and Atmospheric Administration (NOAA) have a long and successful history of weather radar research. The NOAA ground-based radars—WSR-88D network—provide nationwide precipitation observations and estimates with advanced polarimetric capability. As a counterpart, the NASA–JAXA spaceborne radar—the Global Precipitation Measurement Dual-Frequency Precipitation Radar (GPM DPR)—has global coverage and higher vertical resolution than ground-based radars. While significant advances from both NOAA’s WSR-88D network and NASA–JAXA’s spaceborne radar DPR have been made, no systematic comparisons between the WSR-88D network and the DPR have been done. This study for the first time generates nationwide comprehensive comparisons at 136 WSR-88D radar sites from 2014 to 2020. Systematic differences in reflectivity are found, with ground radar reflectivity on average 2.4 dB smaller than that of the DPR (DPR version 6). This research found the discrepancies between WSR-88D and DPR arise from different calibration standards, signal attenuation correction, and differences in the ground and spaceborne scattering volumes. The recently updated DPR version 7 product improves rain detection and attenuation corrections, effectively reducing the overall average WSR-88D and DPR reflectivity differences to 1.0 dB. The goal of this study is to examine the systematic differences of radar reflectivity between the NOAA WSR-88D network and the NASA–JAXA DPR and to draw attention to radar-application users in recognizing their differences. Further investigation into understanding and alleviating the systematic bias between the two platforms is needed.

Urban Flood Runoff Modeling in Japan: Recent Developments and Future Prospects

Since the 20th century, Japan has experienced a period of very rapid urbanization. Cities have experienced substantial densification and expansion, resulting in gradually elevated flood risk. Urban flooding has also occurred in most large cities in Japan, particularly in Tokyo. In response to this growing problem, much effort and resources have been spent on research and development aimed at understanding, simulating, and managing urban flood risk in Japan. The objective of this review is to summarize, discuss, and share key outputs from some of the main research directions in this field, significant parts of which have been uniquely developed in Japan and only published in Japanese. After a general introduction to urban runoff modeling, in the next section, key historical works in Japan are summarized, followed by a description of the situation in Japan with respect to observations of precipitation and water level. Then, the storage function model approach is reviewed, including an extension to urban basins, as well as recent experiments with AI-based emulation in Japanese basins. Subsequently, we review the prospects of detailed hydrodynamic modeling involving high-resolution, vector-based Geographical Information System (GIS) data for the optimal description of the urban environment with applications in Tokyo. We conclude the paper with some future prospects related to urban flood risk modeling and assessment in Japan.

Assessment of corrosive attack of Fe9Cr1Mo alloys in pressurised CO2 for prediction of breakaway oxidation

To provide clarity on the poorly-understood mechanism of breakaway oxidation, corrosion of Fe9Cr1Mo steel in pressurised CO2 is quantified and modelled. The temperature range 400–640∘C, relevant to nuclear power plants, is emphasised. Attack is in the form of combined oxide scale growth and internal carburisation of the metal. Carbon activity in the metal at its surface exhibits a strong time dependence consistent with the kinetically-limited transport of carbon due to the slow Boudouard reaction. Breakaway is associated with the approach to saturation of the steel with respect to carbon. Diffusion modelling agrees well with steel carbide precipitation observations.

A dataset of monthly precipitation with resolution of 1 km in China from 1960 to 2020

Precipitation data are greatly valuable in theoretical research and practical application, and have been widely used in hydrological forecasting, agricultural production, ecological protection and other fields. China has a vast territory with large topographic fluctuations, and most meteorological stations are located in densely populated plain areas. Due to the low density of stations, there is a lack of precipitation observation data in some areas such as mountainous areas. Therefore, it is necessary to use the interpolation method to establish high-precision precipitation datasets. Based on the precipitation monitoring data from more than 2,400 meteorological stations on the ground from 1960 to 2020, we used the climate data spatial interpolation software ANUSPLIN to obtain a dataset of monthly precipitation interpolation with a resolution of 1 km in China. According to the comparison between the dataset and the measured precipitation data as well as the hydrological yearbook data in China released by the CRU and Northwest University of agriculture and forestry science and technology, this dataset has the characteristics of high accuracy, high resolution and long time series, with great potential in scientific research and application.

Detection of Bright Band Heights from Vertical Profile of Radar Reflectivity in Akure, South Western Nigeria

Bright band is an important phenomenon that occurs in radar observations of precipitation. It is a layer of intense reflectivity caused by the melting of the snowflakes in the atmosphere. Accurate detection of bright band and its associated properties are essential for the estimation of precipitation rates and characterization of precipitation types as required in Agriculture, Aviation, Navigation, Telecommunication sectors etc. Bright Band Height (BBH) is a vital parameter in the determination of rain height, rain-induced radio signal attenuation and mitigation techniques. This paper presents an empirical study on the measurement of BBH and Zero Degree Isotherm Height (ZDIH) from Micro Rain Radar (MRR) data obtained in Akure, Nigeria. Vertical profile of radar obtained during stratiform rainy events were examined to detect bright bands and its features. Analysis of the result showed that the ZDIH varies between 4.48 and 4.60 km while the BBH also vary from 4.16 to 4.48 km during intense rainy events in September and October 2010. The derived propagation data would be needed for the optimization and improvement of quality of service (QoS) offered by earth-space links during rainy events in Akure and its environs.

Added value of an atmospheric circulation pattern‐based statistical downscaling approach for daily precipitation distributions in complex terrain

AbstractReliable prediction of heavy precipitation events causing floods in a world of changing climate is crucial for the development of appropriate adaption strategies. Many attempts to provide such predictions have already been conducted but there is still much potential for improvement left. This is particularly true for statistical downscaling of heavy precipitation due to changes present in the corresponding atmospheric drivers. In this study, a circulation pattern (CP) conditional downscaling to the station level is proposed which considers occurring frequency changes of CPs. Following a strict circulation‐to‐environment approach we use atmospheric predictors to derive CPs. Subsequently, precipitation observations are used to derive CP conditional cumulative distribution functions (CDFs) of daily precipitation. Raw precipitation time series are sampled from these CDFs. Bias correction is applied to the sampled time series with quantile mapping (QM) and parametric transfer functions (PTFs) as methods being tested. The added value of this CP conditional downscaling approach is evaluated against the corresponding common non‐CP conditional approach. The performance evaluation is conducted by using Kling–Gupta Efficiency (KGE), root mean squared error (RMSE), and mean absolute error (MAE) metrics. In both cases the applied bias correction is identical. Potential added value can therefore only be attributed to the CP conditioning. It can be shown that the proposed CP conditional downscaling approach is capable of yielding more reliable and accurate downscaled daily precipitation time series in comparison to a non‐CP conditional approach. This can be seen in particular for the extreme parts of the distribution. Above the 95th percentile, an average performance gain of +0.24 and a maximum gain of +0.6 in terms of KGE is observed. These findings support the assumption of conserving and utilizing atmospheric information through CPs can be beneficial for more reliable statistical precipitation downscaling. Due to the availability of these atmospheric predictors in climate model output, the presented method is potentially suitable for downscaling precipitation projections.

Comparative Evaluation of Rainfall Forecasts during the Summer of 2020 over Central East China

By using various skill scores and spatial characteristics of spatial verification methods and traditional techniques of the model evaluation tool, the gridded precipitation observation, known as Climate Prediction Center Morphing Technique, gauge observation and three datasets that were derived from local, Shanghai, and Grapes models, respectively, were conducted to assess the 3 lead day rainfall forecast with 0.5 day intervals during the summer of 2020 over Central East China. Results have shown that the local model generally outperforms the other two for the most skill scores but usually with relatively larger uncertainties than the Shanghai model, and it has the least displacement errors for moderate rainfall among the three datasets. However, the rainfall of the Grapes model has been heavily underestimated and is accompanied with a large displacement error. Both the local and Shanghai model can effectively forecast the large-scale convection and rainstorms but over forecast the local convection, while the local model likely over forecasts the local rainstorms. In addition, the Shanghai model slightly favors over forecasting on a broad scale range and a broad threshold range, and the local model slightly misses the rainfall exceeding 100 mm. Generally, for a broadly comparative evaluation on rainfall, the popular dichotomous methods should be recommended when considering reasonable classification of thresholds if the accuracy is highly demanding. In addition, most spatial methods are suggested to conduct with proper pre-handling of non-rainfall event cases. Especially, the verification metrics including spatial characteristic difference information should be recommended to emphasize rewarding the severe events forecast under a global warming background.

Global tropical cyclone precipitation scaling with sea surface temperature

Understanding the relationship between tropical cyclone (TC) precipitation and sea surface temperature (SST) is essential for both TC hazard forecasting and projecting how these hazards will change in the future due to climate change. This work untangles how global TC precipitation is impacted by present-day SST variability (known as apparent scaling) and by long-term changes in SST caused by climate change (known as climate scaling). A variety of datasets are used including precipitation and SST observations, realistic climate model simulations, and idealized climate model simulations. The apparent scaling rates depend on precipitation metric; examples shown here have ranges of 6.1 to 9.5% per K versus 5.9 to 9.8% per K for two different metrics. The climate scaling is estimated at about 5% per K, which is slightly less than the atmospheric moisture scaling based on thermodynamic principles of about 7% per K (i.e., the Clausius–Clapeyron scaling). The apparent scaling is greater than the climate scaling, which implies that the relationship between TC precipitation and present-day SST variability should not be used to project the long-term response of TC precipitation to climate change.