Rainfall Observations Research Articles

Recent changes in the trend, prominent modes, and the interannual variability of Indian summer monsoon rainfall centered on the early twenty-first century

In this study, the recent changes in rainfall trend, prominent modes, and interannual variability of Indian summer monsoon rainfall (ISMR) for the period 1980–2014 is investigated. Rainfall observations showed a decreasing trend during 1980–2000 (period 1) which changed to a significant increasing mode for the period 2001–2014 (period 2). The analysis shows that two prominent changes in the second period can induce the recent increasing trend of ISMR. The first one is the increased easterly wind anomalies from the equatorial Pacific associated with recent cooling in the eastern Pacific, and the second one is the increased cross-equatorial flow in the Indian Ocean as a result of increased land-sea thermal contrast. During the period 2, the dominant mode of ISMR variability increased and the connection with boundary forcing has intensified. The first mode of ISMR became strongly related to central and east Pacific SST (El Nino Southern Oscillation (ENSO)) anomalies after 2000, while its relationship with major SST indices was weak before that. The second mode contributed to around 14% of variability after the 2000s and is strongly related to Indian Ocean Dipole mode. These modes represent the interannual variability of ISMR during both the periods, and it increases considerably in the second period. Another significant difference between the two periods is that during period 1, many of the severe monsoon events are independent of ENSO; contrary during period 2, many were associated with that. Thus along with the significant change in trend, ISMR has stronger interannual variability, which is the prominent mode of Indian summer monsoon rainfall in the recent period.

The impact of rain to observed signal from Chinese Gaofen-3 synthetic aperture radar in typhoons

Gaofen-3 (GF-3), a Chinese civil synthetic aperture radar (SAR) at C-band, has operated since August 2016. Remarkably, several typhoons have been captured by GF-3 around the China Seas over its last two-year mission. In this study, six images acquired in Global Observation (GLO) and Wide ScanSAR (WSC) modes at vertical-vertical (VV) polarization channel are discussed. This work focuses on investigating the observation of rainfall using GF-3 SAR. These images were collocated with winds from the European Centre for Medium-Range Weather Forecasts (ECMWF), significant wave height simulated from the WAVEWATCH-III (WW3) model, sea surface currents from climate forecast system version 2 (CFSv2) of the National Centers for Environmental Prediction (NCEP) and rain rate data from the Tropical Rainfall Measuring Mission (TRMM) satellite. Sea surface roughness, was compared with the normalized radar cross section (NRCS) from SAR observations, and indicated a 0.8 correlation (COR). We analyzed the dependences of the difference between model-simulated NRCS and SAR-measured NRCS on the TRMM rain rate and WW3-simulated significant wave height. It was found that the effects of rain on SAR damps the radar signal at incidence angles ranging from 15° to 30°, while it enhances the radar signal at incidence angles ranging from 30° to 45° and incidence angles smaller than 10°. This behavior is consistent with previous studies and an algorithm for rain rate retrieval is anticipated for GF-3 SAR.

Assessment of meteorological droughts over Saudi Arabia using surface rainfall observations during the period 1978\u20132017

This paper discusses the preliminary results of meteorological drought analysis over Saudi Arabia for the period 1978–2017. In conjunction with meteorological observations, datasets from the Climate Prediction Center (CPC), the Merged Analysis of Precipitation (CMAP), the Climatic Research Unit (CRU), and the Tropical Rainfall Measuring Mission (TRMM) are utilized to understand the impact of the spatial distribution of rainfall on drought events. Applying precipitation thresholds allows rainfall classifications such as deficit, scanty, and surplus. Precipitation thresholds are also used to define meteorological droughts in the country, which are categorized as usual, moderate, and severe. It is found that drought events occur in Saudi Arabia due to shortfalls in the dry season, even though there is above normal rainfall in the wet season. There is no case of a shortfall in both the wet and dry seasons causing drought. Saudi Arabian droughts of all categories occurred mostly in the dry season, with fewer in the wet season. Results show that in Saudi Arabia, the last month of the wet season (April) is less prone to drought while the first and last months of the dry season (June and September respectively) are more prone to drought. Spatial distribution of drought climatology is obtained by calculating the Standardized Precipitation Index (SPI) and the Palmer Drought Severity Index (PDSI). Further application-driven studies of projections are needed based on drought indices and climate model output.

Using Physically Based Synthetic Peak Flows to Assess Local and Regional Flood Frequency Analysis Methods

AbstractAlthough prior studies have evaluated the role of sampling errors associated with local and regional methods to estimate peak flow quantiles, the investigation of epistemic errors is more difficult because the underlying properties of the random variable have been prescribed using ad‐hoc characterizations of the regional distributions of peak flows. This study addresses this challenge using representations of regional peak flow distributions derived from a combined framework of stochastic storm transposition, radar rainfall observations, and distributed hydrologic modeling. The authors evaluated four commonly used peak flow quantile estimation methods using synthetic peak flows at 5,000 sites in the Turkey River watershed in Iowa, USA. They first used at‐site flood frequency analysis using the Pearson Type III distribution with L‐moments. The authors then pooled regional information using (1) the index flood method, (2) the quantile regression technique, and (3) the parameter regression. This approach allowed quantification of error components stemming from epistemic assumptions, parameter estimation method, sample size, and, in the regional approaches, the number of pooled sites. The results demonstrate that the inability to capture the spatial variability of the skewness of the peak flows dominates epistemic error for regional methods. We concluded that, in the study basin, this variability could be partially explained by river network structure and the predominant orientation of the watershed. The general approach used in this study is promising in that it brings new tools and sources of data to the study of the old hydrologic problem of flood frequency analysis.

Reconstruction of July 2006 Heavy Rainfall Event Series in the Chungju Dam Watershed using a Regional Climate Model

The approach that uses a regional climate model (RCM) is emphasized to prevent and reduce extreme rainfall events that are becoming more powerful due to climate change. This paper presented a preliminary study to produce Probable Maximum Precipitation (PMP) or mega-disaster scenarios using RCMs in Korea. The heavy rainfall event in July 2006, during which the highest average precipitation during the Changma season was recorded, was reconstructed using the Weather Research and Forecasting (WRF) model for the Chungju Dam watershed. Even when considering errors in the rainfall observation data, the WRF model could successfully reconstruct the July 2006 heavy rainfall event. It is expected that more reliable results will be obtained if the flow observation data is used in combination with a runoff model. Keywords: Extreme Rainfall Event, Regional Climate Model, Changma, WRF

Monthly and annual variation of some atmospheric parameters over Aliero, Kebbi State, Nigeria

Temperature changes, relative humidity and changes in annual rainfall can exert a negative effect on the agricultural productivity and radio refractivity in the atmosphere. In this study, the variation of some atmospheric parameters over Aliero town (Latitude 12.306 0N, Longitude 4.492 0E) was examined. The measured data concerns averages of maximum air temperature, relative humidity and amount of rainfall for Aliero. The Data used was obtained from the Meteorological Station of the Kebbi State University of Science and Technology Aliero, for the period of 3 years (2014 – 2016). The analysis was done based on the monthly and annual variations of all the parameters for each year. The results from this study, reveals that the monthly observations of maximum rainfall in Aliero ranges from ~ 17 to 22 mm and the peak occurs in the month of July, and that of relative humidity (~98 to 99%) the peak occurs in the month of August. Atmospheric temperatures in Aliero were observed to be relatively high, with peak values of about 40 to 41°C recorded in the month of April in all the three years considered. While the variation in rainfall and relative humidity were observed to be simultaneous; Air temperature reduces with increase in the amount of rainfall and relative humidity. Averagely, higher amount of rainfall and temperature were recorded in 2016 and that of relative humidity occurred in 2015. These results are of great significance in; understanding the atmospheric condition of Aliero, which has direct impact in agricultural production, tropospheric radio wave propagation and relevant research purposes.

Exploring trends in wet-season precipitation and drought indices in wet, humid and dry regions

This study examines wet season droughts using eight products from the Frequent Rainfall Observations on GridS database. The study begins by evaluating wet season precipitation totals and wet day counts at seasonal and decadal time scales. While we find a high level of agreement among the products at a seasonal time scale, evaluations of 10 year variability indicate substantial non-stationary inter-product differences that make the assessment of low-frequency changes difficult, especially in data-sparse regions. Some products, however, appear more reliable than others on decadal time scales. Global time series of dry, middle, and wet region standardized precipitation index time series indicate little coherent change. There is substantial coherence in year-to-year variations in these time series for the better-performing products, likely indicative of skill for monitoring variations at large spatial scales. During the wet season, the data do not appear to indicate widespread global changes in precipitation, reference evapotranspiration (RefET) or Standardized Precipitation Evapotranspiration Index (SPEI) values. These data also do not indicate a global shift towards increasing aridity. Focusing on SPEI values for dry regions during droughts, however, we find modest increases in RefET and decreases in SPEI when wet season precipitation is below normal. Dry region SPEI values during droughts have decreased by −0.2 since the 1990s. The cause of these RefET increases is unclear, and more detailed analysis will be needed to confirm these results. For wet regions, however, the majority of products appear to indicate increases in wet season precipitation, although many products perform poorly in these regions due to limited observation networks, and estimated increases vary substantially. Synopsis: Our analysis indicates a lack of increasing aridity at global scales, issues associated with non-stationary systematic errors, and concerns associated with increases in reference evapotranspiration in global dry regions during droughts.

A Case Study of Rainfall and Temperature Trends in San Diego Region, 1985–2017

Prolonged and frequent droughts in Southern California present hazards and uncertainty for the region’s increasing population, resulting in proactive and aggressive water management strategies. The goal of this study is to present a case study of the San Diego region’s rainfall and temperature time series analysis in order to determine annual and seasonal trends and their significance. Rainfall and temperature data from 20 rain-gauged stations were analyzed for the period 1985–2017. A project database was set up for data compilation and quality control, and a Mann-Kendall test for trend analysis was used. Results indicated that rainfall in the region decreased both annually and during the rainy season (November–April) by up to 0.14 mm between 1985 and 2017, although not in a statistically significant manner, except at two rainfall observation stations. Rainfall appears to have increased in many of the stations examined during the dry season (May–October), with an average magnitude of 0.09 mm. Analysis of daily minimum and maximum temperature reveals overall average annual and seasonal increases of 0.07 °C and 0.04 °C, respectively, with statistically significant increases at 10 of 17 for minimum temperature, and 0.27 °C and −0.25 °C with statistically significant increases at 9 of 16 for maximum temperature. Temperature tends to have increased more during the dry season compared to the rainy season. This study reveals an overall decreasing tendency in rainfall and an increasing tendency in minimum and maximum temperatures (although not statistically significant) in the San Diego region between 1985 and 2017, which likely contributed to important management implications for the region’s water resources.

Mapping of precipitation in extreme rainfall events in Zhuhai, China

Floods caused by extreme rainfall events have been a predominant water-related issue to coastal cities in Pearl River Delta (PRD), China. In 2017 and 2018, Typhoon Hato and Mangkhut hit the area consecutively, caused severe damages in Zhuhai as well as in other adjacent cities. To combat the inverse impact of these extreme events, adequate emergency response, infrastructure upgrade, and appropriate urban planning are necessary to against the potential extreme rainfalls in future, but all depending on the standards of urban flood protection and storm-water drainages. Precipitation amounts responding to given return-periods are essential parameters for computing the required standards; however, due to the lack of long-term historical observational precipitation records in Zhuhai, it is difficult to precisely predict the value, then a rough prediction was applied in the whole city without considering the spatial variation of extreme precipitation. To specify the spatial distribution of precipitations for given return-periods in Zhuhai, ground-based rainfall observation were analyzed with combined of generalized extreme value (GEV) model and spatial interpolation techniques to compute the precipitation responding to varied given return-periods. The spatial distributions of precipitation in given return-periods offer a more accurate and spatial varied prediction. The information provides reasonable support for Zhuhai’s city manager to conduct proper strategies for emergency response, infrastructure upgrade, and future urban planning to against extreme rainfall events.

Model of surface runoff estimation on oil palm plantation with or without biopore infiltration hole using SCS-CN and ANN methods

Surface runoff by rainfall on oil palm-cultivated land can cause erosion. Method conservation of making biopore infiltration hole on sloping oil palm plantations land has been shown to reduce this runoff. It has been experimentally proven in plots with or without biopores. However, the estimation model of the surface runoff event has not been studied comprehensively. The main novelty of this work is using surface runoff and rainfall observation directly database on oil palm land for the predicted runoff. Estimates model of surface runoff on oil palm plantations are essential to know so that farmers can determine which land conservation is more appropriate in the order they can do forecasting and preparedness for extreme events in their land. In the current study, this paper report to compare two methods of estimating surface runoff model on oil palm land that are given biopores and without biopores using (i) conservation service soil - curve number (SCS-CN) and (ii) artificial neural network (ANN) back-propagation. Thirty data of rainfall and runoff events in the three months were used as input data. The results show that the ANN method can provide a more accurate prediction of surface runoff than the SCS-CN method.

Observation of Short Duration Rainfall in Yogyakarta

On the observation of hourly rainfall data in Java Island, for the modelling watershed purpose, it can be seen that short duration rainfall events are the most dominant. The percentage of short duration rainfall event is almost 70% of the observation data. By using the high resolution of hourly rainfall data with 5 minutes’ intervals, it can be easily to describe the rainfall distribution patterns that occur. This research observed high resolution of hourly rainfall data in hilly and mountainous at Mount Merapi area in Yogyakarta. It purposed to mitigation effort due to the rainfall events that often falls with short duration and high intensity.

Physical Parameterization of IDF Curves Based on Short-Duration Storms

Intensity–duration–frequency (IDF) curves are empirical mathematical formulations that have been used for years in engineering for planning, design, and operation of hydraulic projects. The expression proposed by Sherman (1931) has been validated and used largely by many researchers. In all cases, the four parameters of this formulation are obtained through a numerical procedure. Although these parameters are obtained from historical rainfall observations, the optimization of these parameters implies an infinite combination between them and all those solutions would be valid. Of the four parameters, only one of them (C) has units, and for this reason, a physical sense of parameter C is searched for. Having certainty that some of them can be measured in situ would represent a great advance for modern hydrology. With data from 523 storms monitored every minute, a parametric adjustment was made to the Sherman equation and the typical duration of storms at each site was also obtained. To demonstrate how rainfall intensities vary with the change in C value, rainfall intensities calculations for of 5, 10, 15, and 20 min rainfall duration are used to validate the proposed methodology. The results show that typical storm duration is correlated with the additive parameter of Sherman’s formula.

Three Parties Partnership Between BMKG, Government Institution and General Public on Management of Rainfall Observations Networks in South Sulawesi

Proper rainfall observation is required to obtain the right and accurate weather information. At present the observation of rainfall in Indonesia is managed by various parties and coordinated by the Meteorology, Climatology and Geophysics Agency (BMKG). BMKG has rainfall observation networks that spread throughout Indonesia. But, in terms of quantity and distribution, the current rainfall observation network is not ideal yet. It is necessary to increase the density of the observation network. To increase the density of rainfall collection networks, BMKG needs to establish partnerships with all parties including government agencies and the general public. This study attempts to solve the ideal partnership model for rainfall management in Indonesia, especially in South Sulawesi. Three partnership variables, namely motivation, consistency and sustainability, and two determinant variables, namely coaching and incentives used in this study. From the results of the data analysis it was found that the ideal partnership model was joint operations and added with an intensive coaching element to the observers.

Clustering of Precipitation Pattern in Indonesia Using TRMM Satellite Data

This paper identifies the climatic regions in Indonesia based on the rainfall pattern similarity using TRMM data. Indonesia is a tropical climate region with three main climate clusters, i.e. monsoonal, anti-monsoonal and semi-monsoonal. The clusters were formed by examining rainfall observation datasets recorded at a number of stations over Indonesia with coarse spatial resolution. Clustering based on higher resolution datasets is needed to characterize the rainfall pattern over remote areas with no stations. TRMM provides a high resolution gridded dataset. A statistical test has been applied to evaluate the significance of TRMM bias, and it indicated that the TRMM based satellite precipitation product is a reasonable choice to be used as an input to cluster regions in Indonesia based on the similarity of rainfall patterns. The clustering by Euclidean distance revealed that Indonesia can be grouped into three significantly different rainfall patterns. Compared to the existing references, there have been regions where the rainfall pattern has been shifted. The results in this research thus update the previously defined climate regions in Indonesia.

Quality Control for Crowdsourced Personal Weather Stations to Enable Operational Rainfall Monitoring

AbstractAutomatic personal weather stations owned and maintained by weather enthusiasts provide spatially dense in situ measurements that are often collected and visualized in real time on online weather platforms. While the spatial and temporal resolution of this data source is high, its rainfall observations are prone to typical errors, currently preventing its large‐scale, real‐time application. This study proposes a quality control methodology consisting of four modules targeting these errors, applicable in real time without requiring auxiliary measurements. The quality control improves the overall accuracy of a year of hourly rainfall depths in Amsterdam to a bias of −11.3% (0.2% when a proxy for overall rainfall underestimation by personal weather stations is used), a Pearson correlation coefficient of 0.82, and a coefficient of variation of 2.70, while maintaining 88% of the original data set. Application on a national scale (average 1 station per ∼10 km2) yields high‐resolution nationwide rainfall maps, hence showing the great potential of personal weather stations for complementing existing often sparse traditional rain gauge networks.

Estimating daily inflows of large lakes using a water‐balance‐based runoff coefficient scaling approach

AbstractDaily river inflow time series are highly valuable for water resources and water environment management of large lakes. However, the availability of continuous inflow data for large lakes is still relatively limited, especially for large lakes situated within humid plain regions with tens or even hundreds of tributaries. In this study, we choose the fifth largest freshwater Lake Chaohu in China as our study area to introduce a new approach to reconstruct historical daily inflows at ungauged subcatchments of large lakes. This approach makes use of water level, lake surface rainfall, evaporation from the lake, and catchment rainfall observations. Rainfall–runoff relationship at a reference catchment was analysed to select rainfall input and estimate run‐off coefficient firstly, and the run‐off coefficient was then transferred to ungauged subcatchments to initially estimate daily inflows. Run‐off coefficient was scaled to adjust daily inflows at ungauged subcatchments according to water balance of the lake. This approach was evaluated using sparsely measured inflows at eight subcatchments of Lake Chaohu and compared with the commonly used drainage area ratio method. Results suggest that the inflow time series reconstructed from this approach consistent well to corresponding observations, with mean R2 and Nash–Sutcliffe efficiency values of 0.69 and 0.6, respectively. This approach outperforms drainage area ratio method in terms of mean R2 and Nash–Sutcliffe efficiency values. Accuracy of this approach holds well when the number of water‐level station being used decreased from four to one.

FROGS: a daily 1° × 1° gridded precipitation database of rain gauge, satellite and reanalysis products

Abstract. We introduce the Frequent Rainfall Observations on GridS (FROGS) database (Roca et al., 2019). It is composed of gridded daily-precipitation products on a common 1∘×1∘ grid to ease intercomparison and assessment exercises. The database includes satellite, ground-based and reanalysis products. As most of the satellite products rely on rain gauges for calibration, unadjusted versions of satellite products are also provided where available. Each product is provided over its length of record and up to 2017 if available. Quasi-global, quasi-global land-only, ocean-only and tropical-only as well as regional products (over continental Africa and South America) are included. All products are provided on a common netCDF format that is compliant with Climate and Forecast (CF) Convention and Attribute Convention for Dataset Discovery (ACDD) standards. Preliminary investigations of this large ensemble indicate that while many features appear robust across the products, the characterization of precipitation extremes exhibits a large spread calling for careful selection of the products used for scientific applications. All datasets are freely available via an FTP server and identified thanks to the DOI: https://doi.org/10.14768/06337394-73A9-407C-9997-0E380DAC5598.

An Evaluation Graph of Hourly Rainfall Estimation in Malang

Satellite-based rainfall estimation is evolving rapidly. Most studies use data, which is spatially fine, but poorly regarding time. On the other hand, availability of verification data is also quite rare. This study used Hillman Form B report that was corrected by ME-48 from Malang Climatological Station. 2009-2016 IR1 satellite data were used in hourly temporal resolution (only less than 3% data missing). Four estimation methods were compared: Auto Estimator, CST, mCST, and Quantile Analysis Equation. Data processing was carried out using Python and R statistic as a quality control. The analysis was done by creating a graph that combines False Alarm and Miss Information for each rainfall intensity. Binary transformation was done for enabling information to be plotted. All rainfall estimation methods have a high false alarm (more than 74% at 1 mm) but quite low miss (less than 0.03%). By taking into account its error pattern, satellite data can be used in rainfall observation. The Quantile equation is slightly superior to other methods. This study is relatively inexpensive to be duplicated so it can be used as an evaluation tool for rainfall estimation best practice for Meteorological and Climatological Agency’s network.

Comparative analysis of two rainfall retrieval algorithms during extreme rainfall event: a case study on cloudburst, 2010 over Ladakh (Leh), Jammu and Kashmir

Intense rainfall events lead to floods and landslides in the Western Himalayas (WH). These rainfall amounts are considered comparatively moderate over the plains. These events, called ‘cloudbursts,’ are convective triggered followed by orographically locked phenomena producing sudden high-intensity rainfall over a small area. Early warning and prediction of such severe local weather systems is important to mitigate societal impacts arising from the accompanying flash floods, landslides, etc. Due to lack of ground-based observations, cloudbursts over remote and unpopulated hilly areas often go unreported. Present study examines a cloudburst occurred at Ladakh (Leh) in the WH in the early hours of August 5, 2010, using remotely sensed rainfall data from Tropical Rainfall Measuring Mission (TRMM) and Kalpana-1. The storm lasted for 2 days starting from August 3, 2010, followed by flash floods. Rain-band propagation over the region is studied from Kalpana-1 3-hourly rainfall estimates using Indian Satellite (INSAT) multi-spectral rainfall algorithm (IMSRA) and TRMM rainfall estimates using TRMM 3B42 algorithm. Quantitative and qualitative assessment and comparison of these two products is made. It is observed that there is decrease in satellite brightness temperature (BT) during the rainfall event. Initiation of rainfall occurs at about < 255 K. Maximum of 16.75 mm/h rainfall is observed over the Jammu and Kashmir at 21 GMT from TRMM 3B42 estimates. In addition, it is observed that Kalpana-1 IMSRA underestimates the rainfall observations with respect to Indian Meteorological Department (IMD) rainfall estimates. Spatial correlation at 5% significant level is evaluated, and similarities in rainfall estimates based on rainfall retrieval algorithms are made during the cloudburst event. Mean and standard deviations depict that TRMM 3B42 and IMD rainfall estimates are closer in terms of spatial signature, but estimates of rainfall from Kalpana-1 underestimate the mean and standard deviation signature. In view of orographic contribution, it has been observed that linear fit is the better than nonlinear with less rainfall bias at 90% confidence bound over the region.

Comparison of seasonal prediction outputs based on dynamic atmosphere model and observations (case study: seasonal prediction in 2016 - 2017)

Seasonal predictions are considered as an important factor for many sectors in Indonesia, especially for the agriculture sector. In Indonesia, the variability of seasonal characteristics is influenced by various regional atmospheric phenomena, such as monsoon, ENSO, and IOD. With such urgency, this research is conducted using dynamic models to generate accurate medium-term predictions. The dynamical atmospheric model, Cubical Conformal Atmospheric Model (CCAM) is used to produce daily predictions up to 8 months. CCAM result has been tested on major islands in Indonesia such as Java, Sumatra, Sulawesi, Borneo and Papua. It shows that during the rainy season, the value of rainfall predictions is lower than observations. Meanwhile, the rainfall predictions are overestimate when compared to observations during dry season. Nevertheless, the patterns of rainfall predictions are following satellite rainfall observations patterns, especially in Java.