Spring Extreme Precipitation Days in North China and Their Reliance on Atmospheric Circulation Patterns During 1979–2019

The interannual variation of springtime extreme precipitation (SEP) days in North China (NC) and their reliance on atmospheric circulation patterns are studied by using the continuous daily record of 396 rain gauges and the fifth generation of the European Centre for Medium-Range Weather Forecasts atmospheric reanalysis during 1979–2019. The SEP days are defined as the days when at least 10% of rain gauges in NC record daily precipitation no less than 10.5 mm. Results show that the number of SEP days shows large interannual variability but no significant trend in the study period. Using the objective classification method of the obliquely rotated principal analysis in T-mode, we classify the atmospheric circulation into five different patterns based on the geopotential height at 700 hPa. Three circulation patterns all have fronts and are associated with strong southerly wind, leading to 88% of SEP days in NC. The strong southerly wind may provide moisture and dynamic forcing for the frontal precipitation. The interannual variation of SEP days is related with the number of the three above-mentioned dominant circulation patterns. Further analysis shows that the West Pacific pattern could be one of the possible climate variability modes related to SEP days. This study reveals that the daily circulation pattern may be the linkage between SEP days and climate variability modes in NC.

Evaluation of multiple satellite precipitation products for rainfed maize production systems over Vietnam

High-resolution reliable rainfall datasets are vital for agricultural, hydrological, and weather-related applications. The accuracy of satellite estimates has a significant effect on simulation models in particular crop simulation models, which are highly sensitive to rainfall amounts, distribution, and intensity. In this study, we evaluated five widely used operational satellite rainfall estimates: CHIRP, CHIRPS, CPC, CMORPH, and GSMaP. These products are evaluated by comparing with the latest improved Vietnam-gridded rainfall data to determine their suitability for use in impact assessment models. CHIRP/S products are significantly better than CMORPH, CPC, and GsMAP with higher skill, low bias, showing a high correlation coefficient with observed data, and low mean absolute error and root mean square error. The rainfall detection ability of these products shows that CHIRP outperforms the other products with a high probability of detection (POD) scores. The performance of the different rainfall datasets in simulating maize yields across Vietnam shows that VnGP and CHIRP/S were capable of producing good estimates of average maize yields with RMSE ranging from 536 kg/ha (VnGP), 715 kg/ha (CHIRPS), 737 kg/ha (CHIRP), 759 kg/ha (GsMAP), 878 kg/ha (CMORPH) to 949 kg/ha (CPC). We illustrated that there is a potential for use of satellite rainfall estimates to overcome the issues of data scarcity in regions with sparse rain gauges.

A Comparison of Various Correction and Blending Techniques for Creating an Improved Satellite-Gauge Rainfall Dataset over Australia

Satellites offer a way of estimating rainfall away from rain gauges which can be utilised to overcome the limitations imposed by gauge density on traditional rain gauge analyses. In this study, Australian station data along with the Japan Aerospace Exploration Agency’s (JAXA) Global Satellite Mapping of Precipitation (GSMaP) and the Bureau of Meteorology’s (BOM) Australian Gridded Climate Dataset (AGCD) rainfall analysis are combined to develop an improved satellite-gauge rainfall analysis over Australia that uses the strengths of the respective data sources. We investigated a variety of correction and blending methods with the aim of identifying the optimal blended dataset. The correction methods investigated were linear corrections to totals and anomalies, in addition to quantile-to-quantile matching. The blending methods tested used weights based on the error variance to MSWEP (Multi-Source Weighted Ensemble Product), distance to the closest gauge, and the error from a triple collocation analysis to ERA5 and Soil Moisture to Rain. A trade-off between away-from- and at-station performances was found, meaning there was a complementary nature between specific correction and blending methods. The most high-performance dataset was one corrected linearly to totals and subsequently blended to AGCD using an inverse error variance technique. This dataset demonstrated improved accuracy over its previous version, largely rectifying erroneous patches of excessive rainfall. Its modular use of individual datasets leads to potential applicability in other regions of the world.

Comparison of rainfall from ordinary and automatic rain gauges in Karnataka

The establishment of a network of AWS is one of the very important components under modernization programme of IMD. This study discusses the comparison of 24 hrs accumulated rainfall from ordinary and automatic rain gauges in 11 co-located stations of Karnataka. Results show that Bangalore, Gadag, Honnavar, Dharwad, Haveri, Tumkur, Bidar and Kodagu have bias within ±5 mm exhibiting good performance while Chamarajanagar, Raichur and Bijapur have bias within ±20 mm. The correlation coefficient between two datasets is strong and positive for all the stations except Chamarajnagar, Raichur and Bijapur. The t-test shows that the difference between means of two datasets is not statistically significant at 95% confidence. Further, AWS data is able to show changes in various meteorological parameters along with the movement of the cyclone. This has highlighted the utility of AWS data in extreme weather events like a tropical cyclone

Rainfall-runoff modeling using Doppler weather radar data for Adyar watershed, India

Precipitation is a significant input for hydrologic models; so, it needs to be quantified precisely. The measurement with rain gauges gives the rainfall at a particular location, whereas the radar obtains instantaneous snapshots of electromagnetic backscatter from rain volumes that are then converted into rainfall via algorithms. It has been proved that the radar measurement of areal rainfall can outperform rain gauge network measurements, especially in remote areas where rain gauges are sparse, and remotely sensed satellite rainfall data are too inaccurate. The research focuses on a technique to improve rainfall-runoff modeling based on radar derived rainfall data for Adyar watershed, Chennai, India. A hydrologic model called ‘Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS)’ is used for simulating rainfall-runoff processes. CARTOSAT 30 m DEM is used for watershed delineation using HEC-GeoHMS. The Adyar watershed is within 100 km radius circle from the Doppler Weather Radar station, hence it has been chosen as the study area. The cyclonic storm Jal event from 4-8 November, 2010 period is selected for the study. The data for this period are collected from the Statistical Department, and the Cyclone Detection Radar Centre, Chennai, India. The results show that the runoff is over predicted using calibrated Doppler radar data in comparison with the point rainfall from rain gauge stations.

Road crashes and field rainfall data: mathematical modeling for the Brazilian mountainous highway BR-376/PR

Recent studies analyze the influence of rainfall on traffic crashes, indicating that precipitation intensity is an important factor, for modeling crashes occurrence. This research presents a relationship between daily-basis traffic crashes and precipitation, from 2014 to 2018, in a rural mountainous Brazilian Highway (BR-376/PR), where field rain gauges were used to obtain precipitation data. Data modeling considered a Negative Binomial regression for precipitation influence in crash frequency. Separate regression models were estimated to account for the rainfall effect in different seasons, and for different vehicle types. All models analyzed presented a positive relationship between daily rainfall intensity and daily crashes number. This can indicate that generally rainfall presence is a hazardous factor. Different critical seasons for rainfall influence were also highlighted, alerting for the possible necessity of distinct road safety policies concerning seasonality. Finally, for the vehicle type analysis, typically, rainfall seemed to have a greater effect in lighter vehicles. Moreover, results are useful for traffic control, in order to increase safety conditions.

Employment of MM5 in simulating MCSs developed in and around Bangladesh

The Mesoscale Convective Systems (MCSs) produce numerous weather hazards with their variety of forms. The formation mechanism of MCSs is thus important to know for Bangladesh and its surroundings, because this region is one of the heaviest rainfall areas in the tropical zone. The meteorologists are studying and analyzing the formation mechanism of different types of MCSs using radar and satellite observations data. Observations are limited to real time, but for planning purposes projected parameters over a certain period obtained from a mesoscale model is the requirement. Consequently, the motivation of this paper is to obtain the evolution and life cycle of MCSs developed in and around Bangladesh during pre-monsoon period through the simulation by a mesoscale model named MM5. In this work the calibration of MM5 model for different cumulus parameterization has been performed during the pre-monsoon period of this region. In the present study two domains with mesh resolutions 45 km × 45 km and 15 km × 15 km are prepared. MM5 runs using different cumulus parameterizations are carried out for sensitivity test. The precipitation simulated by the model are compared structurally and numerically with that of Tropical Rainfall Measuring Mission (TRMM) data products, available data from radar scan and observed rain-gauges rainfall in Bangladesh. Important features like lifetime, maintenance mechanism, traversed path, propagation speed and direction of MCSs developed during pre-monsoon period of 2002 in and around Bangladesh have been pointed out.

Comparison of Data from Rain Gauges and the IMERG Product to Analyse Precipitation in Mountain Areas of Central Italy

In central Italy, particularly in the Umbria-Marche Apennines, there are some complete, high-altitude weather stations, which are very important for assessing the climate in these areas. The mountain weather stations considered in this study were Monte Bove Sud (1917 m.a.s.l.), Monte Prata (1816 m.a.s.l.) and Pintura di Bolognola (1360 m.a.s.l.). The aim of this research was to compare the differences between the precipitation measured by the rain gauges and the data obtained by satellite using the IMERG algorithm, at the same locations. The evaluation of possible errors in the estimation of precipitation using one method or the other is fundamental for obtaining a reliable estimate of precipitation in mountain environments. The results revealed a strong underestimation of precipitation for the rain gauges at higher altitudes (Monte Bove Sud and Monte Prata) compared to the same pixel sampled by satellite. On the other hand, at lower altitudes, there was a better correlation between the rain gauge value and the IMERG product value. This research, although localised in well-defined locations, could help to assess the problems in rain detection through mountain weather stations.

Using SEEPS with a TRMM-derived climatology to assess global NWP precipitation forecast skill

Monitoring precipitation forecast skill in global Numerical Weather Prediction (NWP) models is an important yet challenging task. Rain gauges are inhomogeneously distributed, providing no information over large swathes of land and the oceans. Satellite-based products on the other hand provide near-global coverage at a resolution of ~10-25 km, but limitations on data quality (e.g. biases) must be accommodated. In this paper the Stable Equitable Error in Probability Space (SEEPS) is computed using a precipitation climatology derived from the Tropical Rainfall Measurement Mission (TRMM) TMPA 3B42 V7 product and a gauge-based climatology, and applied to two global configurations of the Met Office Unified Model (UM). The representativeness and resolution effects on an aggregated SEEPS is explored by comparing the gauge scores, based on extracting the nearest model grid point, to those computed by upscaling the model values to the TRMM grid and extracting the TRMM grid point nearest the gauge location. The sampling effect is explored by comparing the aggregate SEEPS for this subset of ~6000 locations (dictated by the number of gauges available globally) to all land points within the TRMM region of 50°N and 50°S. Finally, the forecast performance over the oceanic areas is compared to performance over land. Whilst the SEEPS computed using the two different climatologies should never be expected to be identical, using the TRMM climatology provides a means of evaluating near-global precipitation using an internally consistent dataset in a climatologically consistent way.

Evaluation of Global Forecast System (GFS) Medium-Range Precipitation Forecasts in the Nile River Basin

Reliable weather forecasts are valuable in a number of applications, such as, agriculture, hydropower, and weather-related disease outbreaks. Global weather forecasts are widely used, but detailed evaluation over specific regions is paramount for users and operational centers to enhance the usability of forecasts and improve their accuracy. This study presents evaluation of the Global Forecast System (GFS) medium-range (1 day – 15 day) precipitation forecasts in the nine sub-basins of the Nile basin using NASA’s Integrated Multi-satellitE Retrievals (IMERG) “Final Run” satellite-gauge merged rainfall observations. The GFS products are available at a temporal resolution of 3-6 hours, spatial resolution of 0.25°, and its version-15 products are available since 12 June 2019. GFS forecasts are evaluated at a temporal scale of 1-15 days, spatial scale of 0.25° to all the way to the sub-basin scale, and for a period of one year (15 June 2019 – 15 June 2020). The results show that performance of the 1-day lead daily basin-averaged GFS forecast performance, as measured through the modified Kling-Gupta Efficiency (KGE), is poor (0 < KGE < 0.5) for most of the sub-basins. The factors contributing to the low performance are: (1) large overestimation bias in watersheds located in wet climate regimes in the northern hemispheres (Millennium watershed, Upper Atbara & Setit watershed, and Khashm El Gibra watershed), and (2) lower ability in capturing the temporal dynamics of watershed-averaged rainfall that have smaller watershed areas (Roseires at 14,110 sq. km and Sennar at 13,895 sq. km). GFS has better bias for watersheds located in the dry parts of the northern hemisphere or wet parts of the southern hemisphere, and better ability in capturing the temporal dynamics of watershed-average rainfall for large watershed areas. IMERG Early has better bias than GFS forecast for the Millennium watershed but still comparable and worse bias for the Upper Atbara & Setit, and Khashm El Gibra watersheds. The variation in the performance of the IMERG Early could be partly explained by the number of rain gauges used in the reference IMERG Final product, as 16 rain gauges were used for the Millennium watershed but only one rain gauge over each Upper Atbara & Setit, and Khashm El Gibra watershed. A simple climatological bias-correction of IMERG Early reduces in the bias in IMERG Early over most watersheds, but not all watersheds. We recommend exploring methods to increase the performance of GFS forecasts, including post-processing techniques through the use of both near-real-time and research-version satellite rainfall products.