Daily Statistics Research Articles

Investigating Precipitation Extreme and Ardebil Temperature Behavior by Statistical Tests

One of the natural disasters that threatens our country and every year billions of Rials damage to agricultural land and areas of natural resources and infrastructures, such as bridges, canals, dams, etc. makes, floods that usually associated with the occurrence of heavy rainfall. The aim of this study was to investigate the behavior of precipitation extreme precipitation and temperature with the help of statistical tests is Ardabil. Two measures atmospheric temperature and precipitation are the most important elements for identification and analysis of climate change identified by research centers and researchers used climate change. For this purpose, the daily data of precipitation, temperature minimum (night) and the maximum daily synoptic stations during the period 1965 to 2015 is Ardabil. The research team identified profiles are presented of climate change and climate change profiles (ETCCDI) was used to analyze the events and climate change. As impressive 12 profiles 20 profile 22 profiles of the precipitation extreme temperature and precipitation extreme of precipitation. For information about events, climate change, statistical analysis has been used by the World Meteorological Organization.

Weather-based predictive models for Diaporthe helianthi ascospore release in Uruguay

Stem canker (SC), caused by Diaporthe helianthi, is the most serious sunflower disease in Uruguay. Yield losses have been estimated up to 75%. Chemical control is one of the strategies used to manage this disease, but fungicide application should be done before symptoms are visible. Ascospores are the primary source of inoculum, they are produced in perithecia which develop in infected stubble and are dispersed by wind to infect plants. As in other monocyclic diseases, quantifying primary inoculum is essential to predict an epidemic. In this study, ascospores were trapped on microscope slides with solid petroleum jelly which were placed on top of flat open cages filled with natural infected stubble. Cages were placed outdoors, slides where replaced twice a week and stained ascospores were counted under the microscope. Our objective was to develop weather-based models to predict ascospore release levels of D. helianthi from infected stubble. Explanatory weather variables were calculated during the seven-day periods prior to each field weekly ascospore count using daily weather station data from La Estanzuela, Uruguay. Then, logistic models were fit to estimate probabilities of having severe or moderate to light levels of ascospore counts. The best models included variables associated to the precipitation and dew-induced wetness frequency, combinated with the simultaneous occurrence of high relative humidity or low thermal amplitude records. Estimating the evolution of ascospore release through the weather-based models might help to guide preventive fungicide applications to control stem canker in Uruguay.

Changes in Extreme Precipitation in the Northeast United States: 1979–2014

Abstract Extreme precipitation can have significant adverse impacts on infrastructure and property, human health, and local economies. This paper examines recent changes in extreme precipitation in the northeast United States. Daily station data from 58 stations missing less than 5% of days for the years 1979–2014 from the U.S. Historical Climatology Network were used to analyze extreme precipitation, defined as the top 1% of days with precipitation. A statistically significant (95% confidence level) increasing trend of the threshold for the top 1% of extreme precipitation events was found (0.3 mm yr−1). This increasing trend was due to both an increase in the frequency of extreme events and the magnitude of extreme events. Rainfall events ≥ 150 mm (24-h accumulation) increased in frequency from 6 events between 1979 and 1996 to 25 events between 1997 and 2014, a 317% increase. The annual daily maximum precipitation, or the highest recorded precipitation amount in a given year, increased by an average of 1.6 mm yr−1, a total increase of 58.0 mm. Decreasing trends in extreme precipitation were observed east of Lake Erie during the warm season. Increasing trends in extreme precipitation were most robust during the fall months of September, October, and November, and particularly at locations further inland. The analysis showed that increases in events that were tropical in nature, or associated with tropical moisture, led to the observed increase in extreme precipitation during the fall months.

Use of Daily Station Observations to Produce High-Resolution Gridded Probabilistic Precipitation and Temperature Time Series for the Hawaiian Islands

AbstractIt is a major challenge to develop gridded precipitation and temperature estimates that adequately resolve the extreme spatial gradients present in the Hawaiian Islands. The challenge is particularly pronounced because the available station networks are irregularly spaced and sparse, creating large uncertainties in gridded spatial meteorological estimates. Here a 100-member, daily ensemble of precipitation and temperature estimates over the Hawaiian Islands for the period 1990–2014 at 1-km grid resolution is developed. First, an intermediary ensemble estimate of the monthly climatological precipitation and temperature is created, and those climatological surfaces are used to inform daily anomaly interpolation. This climatologically aided interpolation (CAI) method extends our initial ensemble system developed for the continental United States. This study demonstrates that direct interpolation of daily precipitation values is inferior to the CAI methodology, particularly over longer time periods (from years to decades). Daily interpolation performs better for short time periods (e.g., 1 month or less) or when the precipitation distribution substantially diverges from climatology. The CAI ensemble is able to reproduce observed precipitation and temperature patterns, including precipitation occurrence. Leave-one-out cross-validation results illustrate that the ensemble has 1) minimal bias for precipitation and temperature; 2) a mean absolute error of 2.5 mm day−1, 1.0 K, and 2.2 K for precipitation and mean and diurnal temperature, respectively; 3) a mean absolute error of 3.3 mm day−1 for the standard deviation of precipitation; and 4) nearly unbiased probability distributions across multiple thresholds of precipitation intensity. Additionally, the ensemble provides estimates of uncertainty across the distributions with increasing uncertainty for higher percentiles.

Evaluation of synoptic‐scale patterns during extreme temperature and precipitation events in Alaska

Synoptic‐scale patterns associated with daily temperature and precipitation extremes in Alaska are identified and evaluated for daily variability in order to understand consistency in forcing mechanisms associated with extreme events as well as the tendency for each pattern to produce an extreme event. Daily station data at five locations for the 29‐year period from 1982 to 2010 are used. The widely recognized ClimDex indices are used to identify extreme high temperature, low temperature, and single‐day precipitation events. Pressure patterns during extreme events are evaluated seasonally for summer (JJA) and winter (DJF) at mean sea level pressure, 700 and 500‐hPa geopotential heights. Temperature extremes are largely characterized by synoptic‐scale patterns associated with substantial warm and cold air advection, and precipitation extremes are associated with moisture advection from the North Pacific. Extreme warm and cold temperature events are generally associated with more consistent daily synoptic‐scale patterns than precipitation. Considerable dissimilarities among daily synoptic‐scale patterns associated with extreme events based on location, type of extreme event and season highlight the need for careful consideration in the practice of applying synoptic‐scale forcing patterns to forecast or downscale extreme weather events in Alaska.

Characterizing the Spatial Scales of Extreme Daily Precipitation in the United States

The spatial extent of an extreme precipitation event can be important for a basin’s hydrologic response and subsequent flood risk, and may yield insights into underlying atmospheric processes. Using a relaxed moving-neighborhood approach, we develop indicator semivariograms based on precipitation records from the Global Historical Climatology Network–Daily (GHCN-D) station network to directly quantify the climatological length scales of extreme daily precipitation over the United States during 1965–2014. We find that the length scales of extreme (90th percentile) daily precipitation events vary both regionally and seasonally. Over the eastern half of the United States, daily extreme precipitation length scales reach 400 km during the winter months, but are approximately half as large during the summer months. The Northwest region, on the other hand, exhibits little seasonal variation, with extreme precipitation length scales of approximately 150 km throughout the year. By leveraging in situ station measurements, our study avoids some of the uncertainties associated with satellite or interpolated precipitation data, and provides the longest climatological assessment of length scales of extreme daily precipitation over the United States to date. Although the length scales that we calculate can be sensitive to station density, neighborhood size, and neighborhood relaxation, we find that the interregional and interseasonal differences in length scales are relatively robust. Our method could be extended to quantify changes in the spatial extent of extreme daily precipitation in the recent past, and to investigate the underlying causes of any changes that are detected.

Spring snow albedo feedback over northern Eurasia: Comparing in situ measurements with reanalysis products

Abstract. This study uses daily observations and modern reanalyses in order to evaluate reanalysis products over northern Eurasia regarding the spring snow albedo feedback (SAF) during the period from 2000 to 2013. We used the state-of-the-art reanalyses from ERA-Interim/Land and the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) as well as an experimental set-up of ERA-Interim/Land with prescribed short grass as land cover to enhance the comparability with the station data while underlining the caveats of comparing in situ observations with gridded data. Snow depth statistics derived from daily station data are well reproduced in all three reanalyses. However day-to-day albedo variability is notably higher at the stations than for any reanalysis product. The ERA-Interim grass set-up shows improved performance when representing albedo variability and generates comparable estimates for the snow albedo in spring. We find that modern reanalyses show a physically consistent representation of SAF, with realistic spatial patterns and area-averaged sensitivity estimates. However, station-based SAF values are significantly higher than in the reanalyses, which is mostly driven by the stronger contrast between snow and snow-free albedo. Switching to grass-only vegetation in ERA-Interim/Land increases the SAF values up to the level of station-based estimates. We found no significant trend in the examined 14-year time series of SAF, but interannual changes of about 0.5 % K−1 in both station-based and reanalysis estimates were derived. This interannual variability is primarily dominated by the variability in the snowmelt sensitivity, which is correctly captured in reanalysis products. Although modern reanalyses perform well for snow variables, efforts should be made to improve the representation of dynamic albedo changes.

An assessment of temporal effect on extreme rainfall estimates

Abstract. This study assesses the temporal behaviour in terms of inter-decadal variability of extreme daily rainfall of stated return period relevant for hydrologic risk analysis using a novel regional parametric approach. The assessment is carried out based on annual maximum daily rainfall series of 180 meteorological stations of Yangtze River Basin over a 50-year period (1961–2010). The outcomes of the analysis reveal that while there were effects present indicating higher quantile values when estimated from data of the 1990s, it is found not to be noteworthy to exclude the data of any decade from the extreme rainfall estimation process for hydrologic risk analysis.

Dynamical analysis of extreme precipitation in the US northeast based on large-scale meteorological patterns

Previous work has identified six large-scale meteorological patterns (LSMPs) of dynamic tropopause height associated with extreme precipitation over the Northeast US, with extreme precipitation defined as the top 1% of daily station precipitation. Here, we examine the three-dimensional structure of the tropopause LSMPs in terms of circulation and factors relevant to precipitation, including moisture, stability, and synoptic mechanisms associated with lifting. Within each pattern, the link between the different factors and extreme precipitation is further investigated by comparing the relative strength of the factors between days with and without the occurrence of extreme precipitation. The six tropopause LSMPs include two ridge patterns, two eastern US troughs, and two troughs centered over the Ohio Valley, with a strong seasonality associated with each pattern. Extreme precipitation in the ridge patterns is associated with both convective mechanisms (instability combined with moisture transport from the Great Lakes and Western Atlantic) and synoptic forcing related to Great Lakes storm tracks and embedded shortwaves. Extreme precipitation associated with eastern US troughs involves intense southerly moisture transport and strong quasi-geostrophic forcing of vertical velocity. Ohio Valley troughs are associated with warm fronts and intense warm conveyor belts that deliver large amounts of moisture ahead of storms, but little direct quasi-geostrophic forcing. Factors that show the largest difference between days with and without extreme precipitation include integrated moisture transport, low-level moisture convergence, warm conveyor belts, and quasi-geostrophic forcing, with the relative importance varying between patterns.

Characteristics and Preliminary Causes of Tropical Cyclone Extreme Rainfall Events over Hainan Island

The characteristics of tropical cyclone (TC) extreme rainfall events over Hainan Island from 1969 to 2014 are analyzed from the viewpoint of the TC maximum daily rainfall (TMDR) using daily station precipitation data from the Meteorological Information Center of the China Meteorological Administration, TC best-track data from the Shanghai Typhoon Institute, and NCEP/NCAR reanalysis data. The frequencies of the TMDR reaching 50, 100 and 250 mm show a decreasing trend [−0.7 (10 yr)−1], a weak decreasing trend [−0.2 (10 yr)−1] and a weak increasing trend [0.1 (10 yr)−1], respectively. For seasonal variations, the TMDR of all intensity grades mainly occurs from July to October, with the frequencies of TMDR - 50 mm and - 100 mm peaking in September and the frequency of TMDR - 250 mm [TC extreme rainstorm (TCER) events] peaking in August and September. The western region (Changjiang) of the Island is always the rainfall center, independent of the intensity or frequencies of different intensity grades. The causes of TCERs are also explored and the results show that topography plays a key role in the characteristics of the rainfall events. TCERs are easily induced on the windward slopes of Wuzhi Mountain, with the coordination of TC tracks and TC wind structure. A slower speed of movement, a stronger TC intensity and a farther westward track are all conducive to extreme rainfall events. A weaker northwestern Pacific subtropical high is likely to make the 500-hPa steering flow weaker and results in slower TC movement, whereas a stronger South China Sea summer monsoon can carry a higher moisture flux. These two environmental factors are both favorable for TCERs.

Co‐Occurrence of Extreme Daily Rainfall in the French Mediterranean Region

AbstractWe propose in this article a statistical framework to study local disparities in the co‐occurrence of extreme rainfall in the French Mediterranean region. We employ a region‐of‐influence approach by studying the likelihood of the 3% largest daily rainfall to occur simultaneously at less than 50 km distance, when moving across the region. Our model uses an anisotropic max‐stable process allowing us to properly represent the co‐occurrence of daily extremes and including the possibility of a preferred direction of co‐occurrence. We use this framework on a dense network composed of almost 900 daily stations spread over a 100,000 km2 region of southern France under a Mediterranean influence, with data back to 1948. This density allows us to study the spatial patterns in the co‐occurrence of extreme rainfall at fine scale and by so to characterize the main precipitation systems leading to extremes in the region. We show in particular that concomitant extremes are the most likely along the crest line of the Massif Central, which is also the area where the magnitude of extremes is among the largest. This may be of concern for flood risk management.

What determines rail transit passenger volume? Implications for transit oriented development planning

Transit oriented development (TOD) has been an important topic for urban transportation planning research and practice. This paper is aimed at empirically examining the effect of rail transit station-based TOD on daily station passenger volume. Using integrated circuit (IC) card data on metro passenger volumes and cellular signaling data on the spatial distribution of human activities in Shanghai, the research identifies variations in ridership among rail transit stations. Then, regression analysis is performed using passenger volume in each station as the dependent variable. Explanatory variables include station area employment and population, residents’ commuting distances, metro network accessibility, status as interchange station, and coupling with commercial activity centers. The main findings are: (1) Passenger volume is positively associated with employment density and residents’ commuting distance around station; (2) stations with earlier opening dates and serving as transfer nodes tend to have positive association with passenger volumes; (3) metro stations better integrated with nearby commercial development tend to have larger passenger volumes. Several implications are drawn for TOD planning: (1) TOD planning should be integrated with rail transit network planning; (2) location of metro stations should be coupled with commercial development; (3) high employment densities should be especially encouraged as a key TOD feature; and (4) interchange stations should be more strategically positioned in the planning for rail transit network.

Detection and Characterization of Pulses in Broadband Seismometers

Abstract Pulsing—caused either by mechanical or electrical glitches, or by microtilt local to a seismometer—can significantly compromise the long‐period noise performance of broadband seismometers. High‐fidelity long‐period recordings are needed for accurate calculation of quantities such as moment tensors, fault‐slip models, and normal‐mode measurements. Such pulses have long been recognized in accelerometers, and methods have been developed to correct these acceleration steps, but considerable work remains to be done in order to detect and correct similar pulses in broadband seismic data. We present a method for detecting and characterizing the pulses using data from a range of broadband sensor types installed in the Global Seismographic Network. The technique relies on accurate instrument response removal and employs a moving‐window approach looking for acceleration baseline shifts. We find that pulses are present at varying levels in all sensor types studied. Pulse‐detection results compared with average daily station noise values are consistent with predicted noise levels of acceleration steps. This indicates that we can calculate maximum pulse amplitude allowed per time window that would be acceptable without compromising long‐period data analysis.

SA-OBS: A Daily Gridded Surface Temperature and Precipitation Dataset for Southeast Asia

This study introduces a new daily high-resolution land-only observational gridded dataset, called SA-OBS, for precipitation and minimum, mean, and maximum temperature covering Southeast Asia. This dataset improves upon existing observational products in terms of the number of contributing stations, in the use of an interpolation technique appropriate for daily climate observations, and in making estimates of the uncertainty of the gridded data. The dataset is delivered on a 0.25° × 0.25° and a 0.5° × 0.5° regular latitude–longitude grid for the period 1981–2014. The dataset aims to provide best estimates of grid square averages rather than point values to enable direct comparisons with regional climate models. Next to the best estimates, daily uncertainties are quantified. The underlying daily station time series are collected in cooperation between meteorological services in the region: the Southeast Asian Climate Assessment and Dataset (SACA&D). Comparisons are made with station observations and other gridded station or satellite-based datasets (APHRODITE, CMORPH, TRMM). The comparisons show that vast differences exist in the average daily precipitation, the number of rainy days, and the average precipitation on a wet day between these datasets. SA-OBS closely resembles the station observations in terms of dry/wet frequency, the timing of precipitation events, and the reproduction of extreme precipitation. New versions of SA-OBS will be released when the station network in SACA&D has grown further.

Long Term Temperature Trends in Daily Station Data: Australia

A month by month trend analysis of more than 100 years of daily maximum (TMAX) and daily minimum (TMIN) temperatures from three weather stations in Australia is presented. The results show warming trends in TMIN for all twelve calendar months at all three stations with observed warming rates ranging from 0.4 to 2.3 degrees Celsius per century. The TMAX data show a combination of warming trends, cooling trends, and no trends with significant differences among stations and among the calendar months so that no coherent conclusion can be drawn with respect to the long term trend in TMAX. Temperature trends in a moving 30-year window indicate that long term linear OLS trends in temperature are the residual product of violent multi-decadal cycles of warming and cooling at rates that are an order of magnitude greater. Detrended correlation analysis failed to establish a relationship between emissions and warming. The strong evidence of warming found in the TMIN data is confounded by its absence in TMAX as no theoretical basis exists for fossil fuel emissions to cause warming in TMIN and not in TMAX.

Atmospheric circulation patterns associated with monthly and daily temperature and precipitation extremes in Alaska

ABSTRACTThis study presents (1) the temporal variations and trends of monthly and daily temperature and precipitation extremes in Alaska, and (2) the synoptic‐scale circulation patterns associated with these extremes. The analysis is based on daily station observations of maximum temperature, minimum temperature and precipitation from 1948–2012 as well as monthly average temperatures and precipitation from 1920–2012 from 13 Alaska climate divisions. The frequencies of daily and monthly temperature extremes show variability and trends over the past 50–90 years consistent with a warming climate in Alaska. This warming is superimposed on low‐frequency variability attributed to the Pacific Decadal Oscillation. Unlike the temperature extremes, occurrences of heavy‐precipitation extremes do not display a coherent signal through time. The changes do not appear to be linked with an overall increase in the variance of temperature or precipitation. Composite analysis of sea level pressure (SLP) anomalies associated with a representative sample of different daily and monthly temperature extremes across Alaska shows that temperature and moisture advection, subject to topographic and coastal influences, is a key driver of these events. Composite SLP fields for daily and monthly extremes are similar, especially in winter, indicating that monthly extremes represent the recurrence of daily extreme patterns during those extreme months. The SLP anomalies were also often reduced in magnitude in the summer events compared with winter, consistent with the lower variance of temperature and SLP during summer.

Downscaling regional climate model outputs for the Caribbean using a weather generator

ABSTRACTLocally relevant scenarios of daily weather variables that represent the best knowledge of the present climate and projections of future climate change are needed by planners and managers to inform management and adaptation to climate change decisions. Information of this kind for the future is only readily available for a few developed country regions of the world. For many less‐developed regions, it is often difficult to find series of observed daily weather data to assist in planning decisions. This study applies a previously developed single‐site weather generator (WG) to the Caribbean, using examples from Belize in the west to Barbados in the east. The purpose of this development is to provide users in the region with generated sequences of possible future daily weather that they can use in a number of impact sectors. The WG is first calibrated for a number of sites across the region and the goodness of fit of the WG against the daily station observations assessed. Particular attention is focussed on the ability of the precipitation component of the WG to generate realistic extreme values for the calibration or control period. The WG is then modified using change factors (CFs) derived from regional climate model projections (control and future) to simulate future 30‐year scenarios centred on the 2020s, 2050s and 2080s. Changes between the control period and the three futures are illustrated not just by changes in average temperatures and precipitation amounts but also by a number of well‐used measures of extremes (very warm days/nights, the heaviest 5‐day precipitation total in a month, counts of the number of precipitation events above specific thresholds and the number of consecutive dry days).

Climatology of Daily Precipitation and Extreme Precipitation Events in the Northeast United States

Abstract This study examines U.S. Northeast daily precipitation and extreme precipitation characteristics for the 1979–2008 period, focusing on daily station data. Seasonal and spatial distribution, time scale, and relation to large-scale factors are examined. Both parametric and nonparametric extreme definitions are considered, and the top 1% of wet days is chosen as a balance between sample size and emphasis on tail distribution. The seasonal cycle of daily precipitation exhibits two distinct subregions: inland stations characterized by frequent precipitation that peaks in summer and coastal stations characterized by less frequent but more intense precipitation that peaks in late spring as well as early fall. For both subregions, the frequency of extreme precipitation is greatest in the warm season, while the intensity of extreme precipitation shows no distinct seasonal cycle. The majority of Northeast precipitation occurs as isolated 1-day events, while most extreme precipitation occurs on a single day embedded in 2–5-day precipitation events. On these extreme days, examination of hourly data shows that 3 h or less account for approximately 50% of daily accumulation. Northeast station precipitation extremes are not particularly spatially cohesive: over 50% of extreme events occur at single stations only, and 90% occur at only 1–3 stations concurrently. The majority of extreme days (75%–100%) are related to extratropical storms, except during September, when more than 50% of extremes are related to tropical storms. Storm tracks on extreme days are farther southwest and more clustered than for all storm-related precipitation days.

Recent changes in Arctic temperature extremes: warm and cold spells during winter and summer

In the Arctic, climate change manifests with the strongest warming trends on the globe, especially in the cold season. It is under debate if climate extremes change similarly strong. Our study provides detailed regional information about two selected temperature extreme indices in the Arctic, namely warm and cold spells in winter and summer. We analyze their temporal evolution and variability from 1979–2013, based on daily station data and ERA-Interim reanalysis. Calculated trends from both datasets suggest a widespread decrease of cold spells in winter and summer of up to −4 days/decade, with regional patches where trends are statistically significant throughout the Arctic. Winter trends are spatially heterogeneous, the reanalysis also shows small areas with statistically significant increases of cold spells throughout Siberia. Calculated changes in warm spells from both datasets are mostly small throughout the Arctic (less than ±1 day/decade) and statistically not significant. Remarkable exceptions are the Lena river basin in winter with a statistically significant decrease of up to −1.5 days/decade and areas in Scandinavia with statistically significant increases of up to 2.5 days/decade in winter and summer (again from both datasets). From the analysis of spell lengths, we find that there are no shifts from longer to shorter spells or vice versa with time, but long cold spells (events lasting for more than 15 days) disappear almost completely after the year 2000. There is a distinct inter-annual and decadal variability in the spells, which hinders the detection of significant trends for all spell categories in all regions.

Long-term trends and variability of total and extreme precipitation in Thailand

Based on quality-controlled daily station data, long-term trends and variability of total and extreme precipitation indices during 1955–2014 were examined for Thailand. An analysis showed that while precipitation events have been less frequent across most of Thailand, they have become more intense. Moreover, the indices measuring the magnitude of intense precipitation events indicate a trend toward wetter conditions, with heavy precipitation contributing a greater fraction to annual totals. One consequence of this change is the increased frequency and severity of flash floods as recently evidenced in many parts of Thailand. On interannual-to-interdecadal time scales, significant relationships between variability of precipitation indices and the indices for the state of El Niño–Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO) were found. These results provide additional evidence that large-scale climate phenomena in the Pacific Ocean are remote drivers of variability in Thailand's total and extreme precipitation. Thailand tended to have greater amounts of precipitation and more extreme events during La Niña years and the PDO cool phase, and vice versa during El Niño years and the PDO warm phase. Another noteworthy finding is that in 2011 Thailand experienced extensive flooding in a year characterized by exceptionally extreme precipitation events. Our results are consistent with the regional studies for the Asia-Pacific Network. However, this study provides a more detailed picture of coherent trends at a station scale and documents changes that have occurred in the twenty-first century, both of which help to inform decisions concerning effective management strategies.