scholarly journals An Evaluation of the Statistics of Rainfall Extremes in Rain Gauge Observations, and Satellite-Based and Reanalysis Products Using Universal Multifractals

2010 ◽  
Vol 11 (2) ◽  
pp. 388-404 ◽  
Author(s):  
Xiaoming Sun ◽  
Ana P. Barros
Keyword(s):  
Time Series ◽  
Extreme Rainfall ◽  
Global Precipitation Climatology Project ◽  
Rain Gauge ◽  
Climatic Data ◽  
Statistics Of Extremes ◽  
Maximum Rainfall ◽  
Climatic Regions ◽  
National Climatic Data Center ◽  
Rainfall Extremes

Abstract Confidence in the estimation of variations in the frequency of extreme events, and specifically extreme precipitation, in response to climate variability and change is key to the development of adaptation strategies. One challenge to establishing a statistical baseline of rainfall extremes is the disparity among the types of datasets (observations versus model simulations) and their specific spatial and temporal resolutions. In this context, a multifractal framework was applied to three distinct types of rainfall data to assess the statistical differences among time series corresponding to individual rain gauge measurements alone—National Climatic Data Center (NCDC), model-based reanalysis [North America Regional Reanalysis (NARR) grid points], and satellite-based precipitation products [Global Precipitation Climatology Project (GPCP) pixels]—for the western United States (west of 105°W). Multifractal analysis provides general objective metrics that are especially adept at describing the statistics of extremes of time series. This study shows that, as expected, multifractal parameters estimated from the NCDC rain gauge dataset map the geography of known hydrometeorological phenomena in the major climatic regions, including the strong orographic gradients from west to east; whereas the NARR parameters reproduce the spatial patterns of NCDC parameters, but the frequency of large rainfall events, the magnitude of maximum rainfall, and the mean intermittency are underestimated. That is, the statistics of the NARR climatology suggest milder extremes than those derived from rain gauge measurements. The spatial distributions of GPCP parameters closely match the NCDC parameters over arid and semiarid regions (i.e., the Southwest), but there are large discrepancies in all parameters in the midlatitudes above 40°N because of reduced sampling. This study provides an alternative independent backdrop to benchmark the use of reanalysis products and satellite datasets to assess the effect of climate change on extreme rainfall.

The Impact of Trade Wind Strength on Precipitation over the Windward Side of the Island of Hawaii

2008 ◽  
Vol 136 (3) ◽  
pp. 913-928 ◽  
Author(s):  
Mary Ann Esteban ◽  
Yi-Leng Chen
Keyword(s):  
Daily Rainfall ◽  
Early Morning ◽  
Rain Gauge ◽  
Windward Side ◽  
Trade Wind ◽  
Climatic Data ◽  
Maximum Rainfall ◽  
Late Afternoon ◽  
National Climatic Data Center ◽  
The Impact

Abstract The effects of trade wind strength and the diurnal heating cycle on the production of summer trade wind rainfall on the windward side of the island of Hawaii are examined from the data collected from the Hawaiian Rainband Project (HaRP) during 11 July–24 August 1990 and from National Weather Service Hydronet and National Climatic Data Center rain gauge data during 11 July–24 August for the years 1997–2000. For strong trades, the daily rainfall totals on the windward lowlands west of Hilo are higher with a nocturnal maximum there due to the convergence of the katabatic flow and the incoming decelerating trade wind flow, and orographic lifting aloft. The maximum rainfall axis shifts farther inland when trades are stronger. Except in the late afternoon hours, rainfall amounts on the windward side are higher when trades are stronger. For weak trades (≤5 m s−1), the rainfall distributions have a pronounced late afternoon maximum on the windward slopes due to the development of anabatic winds. The nocturnal rainfall over the windward lowlands and the early morning coastal rainfall are lower when trades are weaker.

scholarly journals Analysis of the Behavior of Daily Maximum Rainfall within the Department of Atlántico, Colombia

Water ◽  
2019 ◽  
Vol 11 (12) ◽  
pp. 2453
Author(s):  
Orlando M. Viloria-Marimón ◽  
Álvaro González-Álvarez ◽  
Javier A. Mouthón-Bello
Keyword(s):  
Time Series ◽  
Daily Rainfall ◽  
Information Criterion ◽  
Rain Gauge ◽  
Caribbean Region ◽  
Daily Maximum ◽  
Maximum Rainfall ◽  
Rain Gauges ◽  
Stationary Conditions ◽  
Areal Rainfall

In the Colombian Caribbean region, there are few studies that evaluated the behavior of one of the most commonly used variables in hydrological analyses: the maximum daily rainfall (Pmax-24h). In this study, multiannual Pmax-24h time series from 19 rain gauges, located within the department of Atlántico, were analyzed to (a) determine possible increasing/decreasing trends over time, (b) identify regions with homogeneous behavior of Pmax-24h, (c) assess whether the time series are better suited under either a stationary or non-stationary frequency analysis, (d) generate isohyetal maps under stationary, non-stationary, and mixed conditions, and (e) evaluate the isohyetal maps by means of the calculation of areal rainfall (Pareal) in nine watersheds. In spite of the presence of both increasing and decreasing trends, only the Puerto Giraldo rain gauge showed a significant decreasing trend. Also, three regions (east, central, and west) with similar Pmax-24h behavior were identified. According to the Akaike information criterion test, 79% of the rain gauges showed better fit under stationary conditions. Finally, statistical analysis revealed that, under stationary conditions, the errors in the calculation of Pareal were more frequent, while the magnitude of the errors was larger under non-stationary conditions, especially in the central–south region.

scholarly journals Radar and Rain Gauge Analysis of the Extreme Rainfall during Hurricane Danny’s (1997) Landfall

2007 ◽  
Vol 135 (5) ◽  
pp. 1869-1888 ◽  
Author(s):  
Jeffrey M. Medlin ◽  
Sytske K. Kimball ◽  
Keith G. Blackwell
Keyword(s):  
Inner Core ◽  
Temporal Distribution ◽  
Extreme Rainfall ◽  
Rain Gauge ◽  
Rainfall Amount ◽  
Mobile Bay ◽  
Maximum Rainfall ◽  
Torrential Rainfall ◽  
First Time

Abstract As a minimal hurricane, Danny moved over Mobile Bay around 0900 UTC 19 July 1997 and became stationary by midmorning, while situated within a synoptic col. Danny then evolved into an asymmetric storm with an intensely convective rainband that produced torrential rainfall through 1200 UTC 20 July 1997. Danny’s center remained <100 km from the National Weather Service (NWS) Weather Surveillance Radar-1988 Doppler (WSR-88D) in Mobile, Alabama, for over 48 h, allowing long-term surveillance of the storm’s inner core. This event marked the first time the tropical Z–R relationship was employed on an operational WSR-88D system during tropical cyclone landfall. A radar-estimated maximum rainfall accumulation of 1097 mm (43.2 in.) was analyzed over southwestern Mobile Bay. A NWS cooperative rain gauge located on Dauphin Island, Alabama, measured 896 mm (35.28 in.). An adjacent standard rain gauge measured the highest rainfall amount of 932 mm (36.71 in.). This paper investigates the spatial and temporal distribution and potential magnitude of Danny’s torrential rainfall episode over coastal Alabama. It is shown that both gauges and radar seriously underestimated event rainfall. An estimate is given for what could have been the true event rainfall amount. In the case of the radar, the WSR-88D Algorithm Testing and Display System is used to obtain a better estimate of rainfall using higher dBZ caps than the operational 50 dBZ. In the case of the tipping-bucket rain gauge, wind and mechanical error estimates were applied in order to quantify rainfall underestimation.

scholarly journals Role of Eastern Ghats Orography and Cold Pool in an Extreme Rainfall Event over Chennai on 1 December 2015

2018 ◽  
Vol 146 (4) ◽  
pp. 943-965 ◽  
Author(s):  
Jayesh Phadtare
Keyword(s):  
Wrf Model ◽  
Extreme Rainfall ◽  
Rain Gauge ◽  
Eastern Ghats ◽  
Cold Pool ◽  
Convective System ◽  
Extreme Rainfall Event ◽  
Pressure System ◽  
Sensitivity Experiment ◽  
Maximum Rainfall

Chennai and its surrounding region received extreme rainfall on 1 December 2015. A rain gauge in the city recorded 494 mm of rainfall within a span of 24 h—at least a 100-yr event. The convective system was stationary over the coast during the event. This study analyzes how the Eastern Ghats orography and moist processes localized the rainfall. ERA-Interim data show a low-level easterly jet (LLEJ) over the adjacent ocean and a barrier jet over the coast during the event. A control simulation with the nonhydrostatic Weather Research and Forecasting (WRF) Model shows that the Eastern Ghats obstructed the precipitation-driven cold pool from moving downstream, resulting in the cold pool piling up and remaining stationary in the upwind direction. The cold pool became weak over the ocean. It stratified the subcloud layer and decelerated the flow ahead of the orography; hence, the flow entered a blocked regime. Maximum deceleration of the winds and uplifting happened at the edge of the cold pool over the coast. Therefore, a stationary convective system and maximum rainfall occurred at the coast. As a result of orographic blocking, propagation of a low pressure system (LPS) was obstructed. Because of the topographic β effect, the LPS subsequently traveled a southward path. In a sensitivity experiment without the orography, the cold pool was swept downstream by the winds; clouds moved inland. In the second experiment with no evaporative cooling of rain, the cold pool did not form; flow, as well as clouds, moved over the orography.

Are the short and intense precipitations in North of Italy affected by a significant trend?

2020 ◽  
Author(s):  
Luigi Cesarini ◽  
Mario L.V. Martina
Keyword(s):  
Linear Trend ◽  
Extreme Rainfall ◽  
Rain Gauge ◽  
Rate Of Change ◽  
Significant Trend ◽  
Spatial And Temporal Patterns ◽  
Clear Trend ◽  
Maximum Rainfall ◽  
Increasing Trend ◽  
Non Linear

<p>The upward trend of temperatures is acknowledged and well documented, this increase in temperature is strictly connected to the rate of change in saturation vapour pressure as described by the Clausius-Clapeyron equation. According to this relationship for every rise of 1°C in the temperature, 7% more water vapour is contained in the saturated air that under the right circumstances may turn into rainfall, enhancing an increase in precipitation intensity.</p><p>This study scope is to identify any statistically significant trend in extreme rainfall and its spatial and temporal patterns and detect which morphological and climatic variables are the main drivers of the variation in the frequency and intensity of extreme rainfall events. The study focuses on the northern part of Italy, this area is of particular interest given by the diverse orography of the territory. After quality checks on the data (record length, missing  values and presence of outliers), 382 meteorological stations were selected that provided annual maximum rainfall series (AMS) for different durations, 1,3,6,12 and 24 hours over the period spanning from 1930 to 2017. Trying to maximize the reliability of the data and focusing on the period during which the global warming seems to rise markedly, we decided to focus the analysis on the period of observation going from 1960 to 2017. Also, the date of occurrence of each observation were retrieved enabling the possibility to perform a seasonality analysis on the precipitation extremes.</p><p>The presence and the significance of trends was investigated through a modified version of the non-parametric test Mann-Kendall that takes into account the effect of autocorrelation in the time series. The magnitude of the trend is instead quantified with the Theil -Sen estimator, a reliable method insensitive to outliers. The trend was also assessed through the innovative trend analysis, a graphical method able to detect also non-linear trend.</p><p>A preliminary assessment of the results returned by the Mann-Kendall test displayed an overall  larger presence of stations exhibiting increasing trend rather than decreasing (ratio 4:1). Moreover, the difference between the number of statistically significant increasing and decreasing trends seems to grow with the duration. These results are, in the vast majority of the cases, in accordance with the outcome returned by the ITA. The relationship between trend and elevation of the stations was investigated through means of scatterplots and non-linear tools, every technique adopted confirmed no correlation between the increasing trend in annual maxima and the altitude of the rain-gauge.  The seasonality was studied through boxplots and by observing the frequency of occurrences in each month.  At first glance, no clear trend or shift in the period of occurrences are observed. Instead, it is pretty clear how the dates of occurrence of shorter events (i.e. 1,3 hours) are concentrated in the summer months (convective events), while for longer duration the frequency of occurrence move towards the autumn months. Lastly, temperature data are getting gathered in order to investigate the possible link between annual maxima series of extremes precipitation and temperature as suggested by the Clausis-Clapeyron relationship.</p>

scholarly journals A New Homogenized Climate Division Precipitation Dataset for Analysis of Climate Variability and Climate Change

2011 ◽  
Vol 50 (6) ◽  
pp. 1187-1199 ◽  
Author(s):  
D. Brent McRoberts ◽  
John W. Nielsen-Gammon
Keyword(s):  
Time Series ◽  
Climate Variability ◽  
Weighted Average ◽  
The United States ◽  
Climatic Data ◽  
Monthly Precipitation ◽  
Precipitation Trend ◽  
Climate Trends ◽  
Climate Division ◽  
National Climatic Data Center

AbstractA new homogeneous climate division monthly precipitation dataset [based on full network estimated precipitation (FNEP)] was created as an alternative to the National Climatic Data Center (NCDC) climate division dataset. These alternative climate division monthly precipitation values were estimated using an equal-weighted average of Cooperative Observer Program stations that contained serially complete time series. Missing station observations were estimated by a procedure that was optimized through testing on U.S. Historical Climate Network stations. Inhomogeneities in the NCDC dataset arise from two principal causes. The pre-1931 estimation of NCDC climate division monthly precipitation from statewide averages led to a significant time series discontinuity in several climate divisions. From 1931 to the present, NCDC climate division averages have been calculated from a subset of available station data within each climate division, and temporal changes in the location of available stations have caused artificial changes in the time series. The FNEP climate division dataset is recommended over the NCDC dataset for studies involving climate trends or long-term climate variability. According to the FNEP data, the 1895–2009 linear precipitation trend is positive across most of the United States, and trends exceed 10% per century across the southern plains and the Corn Belt. Remaining inhomogeneities from changes in gauge technology and station location may be responsible for an artificial trend of 1%–3% per century.

Performance of Multiple Satellite Precipitation Estimates over a Typical Arid Mountainous Area of China: Spatiotemporal Patterns and Extremes

2020 ◽  
Vol 21 (3) ◽  
pp. 533-550 ◽  
Author(s):  
Cheng Chen ◽  
Zhe Li ◽  
Yina Song ◽  
Zheng Duan ◽  
Kangle Mo ◽  
...  
Keyword(s):  
Production Management ◽  
Extreme Rainfall ◽  
Rain Gauge ◽  
Probability Of Detection ◽  
Spatiotemporal Patterns ◽  
Mountainous Area ◽  
Climate Data ◽  
Satellite Precipitation ◽  
Rainfall Extremes ◽  
Precipitation Estimates

AbstractPrecipitation in arid mountainous areas is characterized by low rainfall intensity and large spatial heterogeneity, which challenges satellite-based monitoring by the spaceborne sensors. This study aims to comparatively evaluate the detection ability of spatiotemporal patterns and extremes of rainfall by a range of mainstream satellite precipitation products [TMPA, Climate Hazards Group Infrared Precipitation with Station Data (CHIRPS), and PERSIANN–Climate Data Record (PERSIANN-CDR)] over a typical arid mountainous basin of China, benchmarking against rain gauge data from 2000 to 2015. Results showed that satellite precipitation estimates had relatively low accuracy at the daily scale, while a significant improvement of correlation coefficient (CC; >0.6) and a significant reduction of relative root-mean-square error (RRMSE; <1.0) were found as time scale increases beyond the monthly scale. CHIRPS tended to overestimate the gauge precipitation with positive relative bias (RB), while the negative RB values for TMPA and PERSIANN-CDR indicated there was an underestimation. CHIRPS had the most similar spatial pattern and slope trends of the seasonal precipitation and interannual variations of annual precipitation with gauge observations. With the increase in rainfall rates, the probability of detection (POD) and critical success index (CSI) were reduced and the false alarm ratio (FAR) was increased significantly, demonstrating the limited capability for all the three satellite products for detecting heavy rainfall events. CHIRPS showed the best performance in detecting rainfall extremes compared to TMPA and PERSIANN-CDR, evidenced by the larger CSI values and similar extreme rainfall indices obtained from gauge records. This study provides valuable guidance for choosing satellite precipitation products instead of gauge observations for rainfall monitoring (especially rainfall extremes) and agricultural production management over arid mountainous area.

scholarly journals Trends and Interannual Variability of Extreme Rainfall Indices over Cameroon

2021 ◽  
Vol 13 (12) ◽  
pp. 6803
Author(s):  
Derbetini A. Vondou ◽  
Guy Merlin Guenang ◽  
Tchotchou Lucie Angennes Djiotang ◽  
Pierre Honore Kamsu-Tamo
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Extreme Rainfall ◽  
Annual Rainfall ◽  
Agricultural Activity ◽  
Economic Activities ◽  
Intensity Index ◽  
Climatic Regions ◽  
Expert Team ◽  
Annual Total

Central African citizens are highly vulnerable to extreme hydroclimatic events due to excess precipitation or to dry spells. This study makes use of CHIRPS precipitation data gridded at 0.05° × 0.05° resolution and extended from 1981 to 2019 to analyze spatial variabilities and trends of six extreme precipitation indices defined by the Expert Team on Climate Change Detection and Indices (ETCCDI) over Cameroon. They are the number of wet days (RR1), the simple daily intensity index (SDII), the annual total precipitation from days greater than the 95th percentile (R95ptot), the maximum number of consecutive wet days (CWD), the maximum number of consecutive dry days (CDD), the number of very heavy rainfall (RR20). The standard precipitation index (SPI) time series were also examined in the five agro-climatic regions of the domain. The pattern of annual precipitation was first checked over the entire domain. We obtain a well-known pattern showing a decreased precipitation northward with the highest values around the Atlantic Ocean coast. The analysis shows that all indices represent patterns approximately similar to that of annual rainfall except CDD where the spatial south-north gradient is reversed. RR20 shows the lowest spatial variability. Trend study of RR1 indicates negative values south of the domain and predominated positive values in the northern part, where CDD, on the contrary, shows a decreased trend. The highest trends are observed in the northernmost area for CWD and around the coast for SDII and R95ptot. SPI time series indicate an alternative dry and wet period and the years between 1990 and 2000 witnessed more annual wet conditions. Such a study is very important in this domain where variabilities of climatic components are very high due to climate change impact and diversified relief. The results can serve as a reference for agricultural activity, hydropower management, civil engineering, planning of economic activities and can contribute to the understanding of the climate system in Cameroon.

scholarly journals Extreme Rainfall over Complex Terrain: An Application of the Linear Model of Orographic Precipitation to a Case Study in the Italian Pre-Alps

Geosciences ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 18
Author(s):  
Andrea Abbate ◽  
Monica Papini ◽  
Laura Longoni
Keyword(s):  
Complex Terrain ◽  
Extreme Event ◽  
Extreme Rainfall ◽  
Rain Gauge ◽  
Orographic Precipitation ◽  
Mountain Areas ◽  
Rainfall Extremes ◽  
Realistic Description ◽  
Terrain Elevation

Intense meteorological events are the primary cause of geohazard phenomena in mountain areas. In this paper, we present a study of the intense rainfall event that occurred in the provinces of Lecco and Sondrio from 11 to 12 June 2019. The aim of our work is to understand the effect of local topography on the spatial distribution of rainfall and to attempt the reconstruction of a realistic rainfall field relative to that extreme event. This task represents a challenge in the context of complex orography. Classical rain-gauge interpolation techniques, such as Kriging, may be too approximate, while meteorological models can be complex and often unable to accurately predict rainfall extremes. For these reasons, we tested the linear upslope model (LUM) designed for estimating rainfall records in orographic precipitation. This model explicitly addresses the dependence of rainfall intensification caused by the terrain elevation. In our case study, the available radio sounding data identified the convective nature of the event with a sustained and moist southern flow directed northward across the Pre-Alps, resulting in an orographic uplift. The simulation was conducted along a smoothed elevation profile of the local orography. The result was a reliable reconstruction of the rainfall field, validated with the ground-based rain gauge data. The error analysis revealed a good performance of the LUM with a realistic description of the interaction between the airflow and local orography. The areas subjected to rainfall extremes were correctly identified, confirming the determinant role of complex terrain in precipitation intensification.

scholarly journals Immediate and Delayed Meteorological Effects on COVID-19 Time-Varying Infectiousness in Tropical Cities

Atmosphere ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 513
Author(s):  
Xerxes Seposo ◽  
Chris Fook Sheng Ng ◽  
Lina Madaniyazi
Keyword(s):  
Model Fitting ◽  
Additive Model ◽  
The Philippines ◽  
Air Pressure ◽  
Dew Point ◽  
Outcome Variable ◽  
Climatic Data ◽  
Time Varying ◽  
Rt Pcr ◽  
National Climatic Data Center

The novel coronavirus, which was first reported in Wuhan, China in December 2019, has been spreading globally at an unprecedented rate, leading to the virus being declared a global pandemic by the WHO on 12 March 2020. The clinical disease, COVID-19, associated with the pandemic is caused by the pathogen severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Aside from the inherent transmission dynamics, environmental factors were found to be associated with COVID-19. However, most of the evidence documenting the association was from temperate locations. In this study, we examined the association between meteorological factors and the time-varying infectiousness of COVID-19 in the Philippines. We obtained the daily time series from 3 April 2020 to 2 September 2020 of COVID-19 confirmed cases from three major cities in the Philippines, namely Manila, Quezon, and Cebu. Same period city-specific daily average temperature (degrees Celsius; °C), dew point (degrees Celsius; °C), relative humidity (percent; %), air pressure (kilopascal; kPa), windspeed (meters per second; m/s) and visibility (kilometer; km) data were obtained from the National Oceanic and Atmospheric Administration—National Climatic Data Center. City-specific COVID-19-related detection and intervention measures such as reverse transcriptase polymerase chain reaction (RT-PCR) testing and community quarantine measures were extracted from online public resources. We estimated the time-varying reproduction number (Rt) using the serial interval information sourced from the literature. The estimated Rt was used as an outcome variable for model fitting via a generalized additive model, while adjusting for relevant covariates. Results indicated that a same-day and the prior week’s air pressure was positively associated with an increase in Rt by 2.59 (95% CI: 1.25 to 3.94) and 2.26 (95% CI: 1.02 to 3.50), respectively. Same-day RT-PCR was associated with an increase in Rt, while the imposition of community quarantine measures resulted in a decrease in Rt. Our findings suggest that air pressure plays a role in the infectiousness of COVID-19. The determination of the association of air pressure on infectiousness, aside from the testing frequency and community quarantine measures, may aide the current health systems in controlling the COVID-19 infectiousness by integrating such information into an early warning platform.

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