Mean Daily Precipitation Research Articles

Climatology of Orographic Precipitation Gradients Over High Mountain Asia Derived From Dynamical Downscaling

AbstractWithin High Mountain Asia (HMA), the annual melting of glaciers and snowpack provides vital freshwater to populations living downstream. Precipitation over HMA can directly affect the freshwater availability in this region by altering the mass balance of glaciers and snowpack. However, available reanalyses and downscaling simulations lack the resolution required to understand important glacier‐scale variations in precipitation. This study aimed to determine the current characteristics of orographic precipitation gradients (OPG) by curve‐fitting daily precipitation as a function of elevation from a 15‐year, 4‐km grid spaced Weather Research and Forecasting (WRF) model simulation focused on the Himalayan, Karakoram, and Hindu‐Kush mountain ranges. To facilitate precipitation curve‐fitting, the WRF model grid points were separated into regions of similar orientation, referred to as facets. Akaike Information Criterion‐corrected values and an F‐test p‐value identified the need for a curvature term to account for a varying OPG with elevation. Regions with similar seasonal variability were found using ‐means clustering of the monthly mean OPG coefficients. The central Himalayan slope's intra‐seasonal variability of OPG depended on synoptic scale conditions, in which cyclonically‐forced heavy‐precipitation events produced strong sublinear increases in precipitation with elevation. Initial testing of precipitation estimates using monthly coefficients showed promising results in downscaling daily WRF precipitation; the daily mean absolute error at each grid point had a lower magnitude than the daily mean precipitation total, on average. Results provide a physically‐based context for machine learning algorithms being developed to predict OPG and downscale precipitation output from global climate models over HMA.

Nonlinear impacts of climate change on dengue transmission in mainland China: Underlying mechanisms and future projection

Dengue fever is a climate-sensitive health concern caused by mosquito-borne virus. Both temperature- and precipitation-related factors are important for dengue transmission. Previous studies mainly focused on the effects of individual factors on the spread of dengue. However, given the potential synergistic effects of temperature and precipitation on virus transmission, it is vital to provide a holistic view of their interplay in shaping the transmission patterns. In this study, utilizing a vectorial capacity (VC)-based mathematical model, we investigated the mechanisms by which the daily mean temperature (DMT), diurnal temperature range (DTR) and daily mean precipitation (DMP) jointly influence the dengue spread. We further forecast future dengue patterns in mainland China under different climate scenarios (Shared Socioeconomic Pathways (SSP)) by 2050 and 2080. Results show that these three climate factors trigger complex nonlinear effects on dengue transmission. Interactions between DMT and DTR plays a decisive role in determining the spreading fronts of dengue epidemics, while DMP considerably impacts its transmission intensity. Although future climate changes would facilitate northward expansion of areas at risk of dengue under SSP245 and SSP585, significant contraction would happen under SSP126. These findings illuminate the interacting mechanisms of temperature and precipitation on dengue transmission, call for the comprehensive considerations of DMT, DMP and DTR in formulating effective dengue control policies, and warrant achieving a sustainable future in line with the SSP126 scenario to effectively mitigate dengue transmission risks and ensure a resilient future.

Changes in regional daily precipitation intensity and spatial structure from global reanalyses

AbstractWe conducted an analysis of hydrological cycle variations across 13 regions of varying sizes distributed across different continents. The analysis is based on five reanalysis datasets of daily precipitation, all produced by the European Centre on Medium‐Range Weather Forecasts (ECMWF): ERA5 high‐resolution, ERA5 ensemble, CERA‐20C, ERA20‐C and ERA20‐CM. We examined several climate indicators, including the daily mean precipitation, the 75th and 99th percentiles, the precipitation area fraction and the area fractions with precipitations exceeding 10 and 20 mm. We evaluated the ability of the reanalyses to capture precipitation at specific spatial scales using scale‐separation diagnostics based on 2D wavelet decomposition. The climatological energy spectra of precipitation derived from the analysis describe the scales that each reanalysis can accurately reproduce, serving as a unique signature for each dataset. We compared the spatial scales that were comparable across the different reanalyses and examined the temporal trends of energy on those scales. The results indicate that the hydrological cycle is undergoing changes in all regions, with some variations observed across different regions. Common features include an increase in intense precipitation events and a decrease in the corresponding spatial extent. The ensemble of ERA5 reanalyses exhibited the smallest effective resolution, as determined by the scale‐separation method, and displayed more pronounced trends compared to other reanalyses. Notably, an acceleration of changes is evident in the last 20 years. However, Central Asia may be an exception, showing relatively less noticeable changes in the hydrological cycle.

Analysis of Precipitation Diurnal Cycle and Variance in Multiple Observations, CMIP6 Models, and a Series of GFDL-AM4.0 Simulations

Abstract The diurnal cycle of precipitation and precipitation variances at different time scales are analyzed in this study based on multiple high-resolution 3-h precipitation datasets. The results are used to evaluate nine CMIP6 models and a series of GFDL-AM4.0 model simulations, with the goal of examining the impact of SST diurnal cycle, varying horizontal resolutions, and different microphysics schemes on these two precipitation features. It is found that although diurnal amplitudes are reasonably simulated, models generally generate too early diurnal peaks over land, with a diurnal phase peaking around noon instead of the observed late afternoon (or early evening) peak. As for precipitation variances, irregular subdaily fluctuations dominate the total variance, followed by variance of daily mean precipitation and variance associated with the mean diurnal cycle. While the spatial and zonal distributions of precipitation variances are generally captured by the models, significant biases are present in tropical regions, where large mean precipitation biases are observed. The comparisons based on AM4.0 model simulations demonstrate that the inclusion of ocean coupling, adoption of a new microphysics scheme, and increasing of horizontal resolution have limited impacts on these two simulated features, emphasizing the need for future investigation into these model deficiencies at the process level. Conducting routine examinations of these metrics would be a crucial first step toward better simulation of precipitation intermittence in future model development. Last, distinct differences in these two features are found among observational datasets, highlighting the urgent need for a detailed evaluation of precipitation observations, especially at subdaily time scales, as model evaluation heavily relies on high-quality observations. Significance Statement High-frequency precipitation data, such as 3-hourly or finer resolution, provide detailed and precise information about the intensity, timing, and location of individual precipitation events. This information is essential for evaluating physically based numerical weather and climate models, which are important tools for understanding and predicting precipitation changes. We compared several global high-resolution observation datasets with nine CMIP6 GCMs and a series of GFDL-AM4.0 model simulations to evaluate the precipitation diurnal cycle and variance, with the goal of examining the impact of SST diurnal cycle, varying horizontal resolutions, and different microphysics schemes on these metrics. Despite the impact of these factors on the simulated precipitation diurnal cycle and variance being evident, our results also show that they are not consistently aligned with observed features. This highlights the need for further investigation into model deficiencies at the process level. Therefore, conducting routine examinations of these metrics could be a crucial first step toward improving the simulation of precipitation intermittency in future model development. Additionally, given the large uncertainties, there is an urgent need for a detailed evaluation of observational precipitation products, particularly at subdaily time scales.

Reduction of mosquito communities using the mass trapping method

AbstractThis study was conducted to demonstrate the effect of the mass trapping of mosquitoes as an eco‐friendly physical control method. It was conducted at the Ansan Hwarang Auto Camping Site in Gyeonggi Province, Republic of Korea. The study period was approximately 1 month (from July 2 to July 30, 2018). Inner and outer traps were installed to control mosquitoes inside and outside the study area. The inner trap consisted of 5 digital mosquito monitoring system units placed inside the study area, whereas the outer trap included 26 MOSHOLE‐PRO units installed at 30 m intervals outside the study area. Both the inner and outer traps used carbon dioxide as an attractant. Additionally, the outer traps were grouped close to each inner trap using distance analysis and designated as Sn (n, inner trap number). According to the study results, analysis of the daily mean temperature and daily mean precipitation from 2017 to 2019 showed that the meteorological data were statistically insignificant. During the mass trapping period, the outer traps had the highest mean number of mosquitoes in the S1 group, whereas the other groups exhibited statistically similar results. Analysis of the mean number of mosquitoes in the inner trap confirmed a mean reduction rate of 59.39% through mass trapping, and the results were statistically significant (P < 0.001). This study confirms the effectiveness of mass trapping in reducing mosquito communities and suggests the possibility of creating mosquito‐free zones by targeting mosquitoes both inside and outside the study area.

An Evaluation of CRA40 and ERA5 Precipitation Products over China

Precipitation datasets derived from reanalysis products play a crucial role in weather forecasting and hydrological applications. This paper aims to evaluate the performance of two distinct reanalysis precipitation products, i.e., the first-generation Chinese global land-surface reanalysis precipitation product (CRA40) and the fifth-generation European reanalysis precipitation product (ERA5), over mainland China. The evaluation is based on continuous and categorical statistical indicators with daily-scale gridded-point rain gauge data obtained from Chinese surface meteorological stations. The findings of this study can be summarized as follows: (1) CRA40 demonstrates a clear superiority over ERA5 in terms of the 13-year daily mean precipitation and seasonal daily precipitation. CRA40 exhibits better correlation coefficients (0.97), relative biases (5.25%), root mean square errors (0.34 mm), and fractional standard errors (0.05). (2) Both reanalyzed precipitation products generally exhibit an overestimation of precipitation in mainland China. The degree of overestimation is particularly pronounced in dry climatic regions (e.g., QZ, XJ), while wet regions (e.g., CJ, HN) demonstrate relatively less overestimation. (3) ERA5 shows better performance in the detection of daily precipitation. Neither CRA40 nor ERA5 can capture heavy precipitation events well. These findings are expected to advance our understanding of the strengths and limitations of the reanalysis precipitation products, CRA40 and ERA5, over China.

Climatological trends of mean and extreme daily precipitation in Arizona (USA)

Precipitation in Arizona, USA, represents a fundamental resource for the needs of agriculture, people, and the environment. However, due to the risk associated with flash flooding, extreme precipitation also constitutes a natural hazard. Available research on precipitation in Arizona has been limited in scope and focus and there currently exists no comprehensive assessment of statewide, historical, mean, and extreme precipitation. We use daily precipitation records from 43 Global Historical Climate Network daily (GHCNd) weather stations to examine trends in mean and extreme precipitation across Arizona from 1950 to 2020. We use a suite of standardized precipitation indices and explore the statistical significance of historical changes at the annual, monthly, and seasonal scales. Our analysis returns a motley collection of results displaying a great degree of spatial variability and sensitivity to the temporal scale of analysis. Mean total precipitation at the statewide scale underwent a small decreasing trend of 0.15 mm year−1, with considerable interannual variability being a dominant feature. The majority of stations experienced no statistically significant change in extreme precipitation at the annual scale. Monsoon season mean precipitation followed a similar pattern to annual means, with an overall reduction of 0.14 mm year−1 across the state. One fourth of stations, all located at elevations lower than 650 m, recorded decreases in monsoon precipitation intensity. Winter season analysis presents a different picture, displaying a positive statewide trend of 0.11 mm year−1 in mean precipitation. One tenth of stations recorded increases in extreme precipitation intensity and decreases in the number of consecutive dry days. Results of our observational analysis indicate a lack of a clear consensus on the climatological trends of mean/extreme precipitation in Arizona during the study period. Our findings are in contrast to those from other similarly arid areas across the world and highlight the role of regional differences in modulating the potential hydrometeorological impacts associated with climate change. However, while we do not find widespread, significant, modification in the spectrum of precipitation changes examined, the hydrologic system of the state has been – and will continue to be - impacted by the ongoing temperature increase associated with the buildup of greenhouse gases and continued population growth.

On the Resolution Sensitivity of Equatorial Precipitation in a GFDL Global Atmospheric Model

AbstractWe performed a series of aquaplanet simulations at the horizontal resolution from 50 to 6 km with identical parameterization settings using the Geophysical Fluid Dynamics Laboratory's Atmosphere Model version 4 implemented with the two‐moment Morrison‐Gettelman cloud microphysics with prognostic precipitation (GFDL AM4‐MG2). At the finer resolution, the global mean resolved‐scale precipitation increases and that from cumulus parameterization decreases. The model also simulates less/thinner clouds over the low latitudes and more/thicker clouds over the high latitudes as the resolution increases. The precipitation over the deep tropics is investigated in detail. We find little resolution sensitivity in the daily mean precipitation extremes. Changes of the equatorial resolved precipitation with resolution cannot be fully explained by the resolution dependence in the vertical velocity amplitude. We report a robust sensitivity in the convective organization over the deep tropics to the model resolution. In simulations of finer resolution, the localized convection is suppressed, and the organized convective system associated with large‐scale circulations becomes more prominent.

Minute level smartphone derived exposure to greenness and consumer wearable derived physical activity in a cohort of US women

BackgroundInconsistent results have been found in the literature on associations of greenness, or vegetation quantity, and physical activity. However, few studies have assessed associations between mobility-based greenness and physical activity from mobile health data from smartphone and wearable devices with fine spatial and temporal resolution. MethodsWe assessed mobility-based greenness exposure and wearable accelerometer data from participants in the US-based prospective Nurses’ Health Study 3 cohort Mobile Health (mHealth) Substudy (2018–2020). We recruited 500 female participants with instructions to wear devices over four 7-day sampling periods equally spaced throughout the year. After restriction criteria there were 337 participants (mean age 36 years) with n = 639,364 unique observations. Normalized Difference Vegetation Index (NDVI) data were derived from 30 m x 30 m Landsat-8 imagery and spatially joined to GPS points recorded every 10 min. Fitbit proprietary algorithms provided physical activity summarized as mean number of steps per minute, which we averaged during the 10-min period following a GPS-based greenness exposure assessment. We utilized Generalized Additive Mixed Models to examine associations (every 10 min) between greenness and physical activity adjusting for neighborhood and individual socioeconomic status, Census region, season, neighborhood walkability, daily mean temperature and precipitation. We assessed effect modification through stratification and interaction models and conducted sensitivity analyses. ResultsMean 10-min step count averaged 7.0 steps (SD 14.9) and greenness (NDVI) averaged 0.3 (SD 0.2). Contrary to our hypotheses, higher greenness exposure was associated non-linearly with lower mean steps per minute after adjusting for confounders. We observed statistically significant effect modification by Census region and season. DiscussionWe utilized objective physical activity data at fine temporal and spatial scales to present novel estimates of the association between mobility-based greenness and step count. We found higher levels of greenness were inversely associated with steps per minute.

Performance of the WRF Model at the Convection‐Permitting Scale in Simulating Snowfall and Lake‐Effect Snow Over the Tibetan Plateau

AbstractThis study investigated the performance of the Weather Research Forecasting (WRF) model at 4‐km horizontal grid spacing in simulating precipitation, 2 m air temperature (T2), snowfall, and lake‐effect snow (October 4–8, 2018) over the Tibetan Plateau (TP). Multiple simulations with different physical parameterization schemes (PPSs), including two planetary boundary layer schemes (Yonsei University and Mellor–Yamada–Janjic), no cumulus and multi‐scale Kain‐Fritsch, two land surface models (Noah and Noah‐MP), and two microphysics schemes (Thompson and Milbrandt), were conducted and compared. Compared with gauge observations, all PPSs simulate mean daily precipitation with mean relative errors (MREs) of 27.7%–53.6%. Besides, spatial correlation coefficients (SCCs) between simulated and observed mean daily precipitation range from 0.56 to 0.71. For simulations of T2, all PPSs perform similarly well, even though the mean cold biases are up to about 3°C. Meanwhile, all PPSs exhibit acceptable performance in simulating spatial distributions of snow depth, snow cover, and snowfall amount, with SCCs of 0.37–0.65 between simulations and observations. However, the WRF simulations significantly overestimate snow depth (∼0.4 cm mean error) and snowfall amount (MREs >372%). The Milbrandt scheme slightly outperforms the other PPSs in simulating snow‐related variable magnitudes. Due to their inaccurate temperature and airflow modeling over the lake surface and its surroundings, none of the WRF simulations well reproduce the characteristics that more snow occurs over the lake and downwind area. Overall, this study provides a useful reference for future convection‐permitting climate modeling of snow or other extreme events when using the WRF model in the TP and other alpine regions.

Extreme climatic events to intensify over the Lake Victoria Basin under global warming

This paper presents an analysis of future precipitation patterns over the Lake Victoria Basin, East Africa, using bias-corrected CMIP6 model projections. A mean increase of about 5% in mean annual (ANN) and seasonal [March–May (MAM), June–August (JJA), and October–December (OND)] precipitation climatology is expected over the domain by mid-century (2040–2069). The changes intensify towards the end of the century (2070–2099) with an increase in mean precipitation of about 16% (ANN), 10% (MAM), and 18% (OND) expected, relative to the 1985–2014 baseline period. Additionally, the mean daily precipitation intensity (SDII), the maximum 5-day precipitation values (RX5Day), and the heavy precipitation events—represented by the width of the right tail distribution of precipitation (99p–90p)—show an increase of 16%, 29%, and 47%, respectively, by the end of the century. The projected changes have a substantial implication for the region—which is already experiencing conflicts over water and water-related resources.

Rainfall and gemmiferous shoots influence asexual reproduction in two tropical mosses

Background: For some bryophytes, asexual reproduction may be the primary means of population survival, which can be affected by rainfall and moss colony features. Questions and / or Hypotheses: We investigated i) how gemma output differed in two tropical epiphytic mosses with mainly asexual and sexual-asexual reproduction; ii) how is the pattern of gemmae production through an annual interval; and iii) whether gemma production in both species is correlated with rainfall and colony features (shoot density, number of gemmiferous shoots, and sexual structures) per shoot and area (cm2). Studied species / data description /Mathematical model: Calymperes afzelii Sw. and Syrrhopodon incompletus Schwägr. Study site and dates: The sampling was conducted every two months for a year at Agua Blanca State Park in Tabasco, Mexico. Methods: Squares were used to record the gemma production per shoot and cm2, the number of gemmiferous shoots, and shoots with sexual structures. Daily mean precipitation was obtained from a meteorological station. We analysed the effect of rainfall and colony features on gemmae production at the shoot and cm2 levels using generalized linear models. Results: C. afzelii produced gemmae more constantly than S. incompletus. Gemma production is higher during months of high rainfall. The rainfall and number of gemmiferous shoots are suitable predictors of gemma production. Conclusions: Our results exemplify how rainfall and gemmiferous shoots influence the production of asexual propagules in two mosses with different reproductive strategies in the same tropical environment. This information is essential to understand the population dynamics of tropical mosses.

Machine learning-based modeling of surface sediment concentration in Doce river basin

As sediment measurements are laborious and costly, alternative techniques are required to provide such information from more easily measured variables. Thus, the objective of this study was to use machine learning-based models to predict the surface sediment concentration (SSC) in the Doce river basin. The cross-sectional averages of measurements from seven sediment monitoring stations of the Agência Nacional de Águas e Saneamento Básico located in the Doce riverbed were used as the SSC data. A total of 62 predictor variables were used, which were derived from data on the terrain slope, pedology, land use and cover, precipitation, river discharge and velocity, actual evapotranspiration, surface runoff, soil moisture, temperature, and normalized difference vegetation index. The Boruta and recursive feature elimination variable selection methods were employed to reduce the number of predictor variables. The random forest, Cubist, support vector machine, and eXtreme Gradient Boosting (XGBoost) algorithms as well as least absolute shrinkage and selection operator (LASSO) regression were applied to predict the SSC data. The machine learning algorithms provided superior results, particularly the Cubist and XGBoost models, which exhibited the lowest prediction error and highest efficiency metrics. According to the varImp function from Caret package, the most important predictor variables for the SSC modeling were the daily river discharge on the sediment collection date and time-lagged discharge. The cumulative daily mean precipitation was also important for the sediment modeling. Our findings demonstrate that machine learning models may be a very helpful tool for sediment monitoring and understanding sediment dynamics in the Doce river basin over time.

Machine Learning Prediction Model of Tuberculosis Incidence Based on Meteorological Factors and Air Pollutants.

Tuberculosis (TB) is a public health problem worldwide, and the influence of meteorological and air pollutants on the incidence of tuberculosis have been attracting interest from researchers. It is of great importance to use machine learning to build a prediction model of tuberculosis incidence influenced by meteorological and air pollutants for timely and applicable measures of both prevention and control. The data of daily TB notifications, meteorological factors and air pollutants in Changde City, Hunan Province ranging from 2010 to 2021 were collected. Spearman rank correlation analysis was conducted to analyze the correlation between the daily TB notifications and the meteorological factors or air pollutants. Based on the correlation analysis results, machine learning methods, including support vector regression, random forest regression and a BP neural network model, were utilized to construct the incidence prediction model of tuberculosis. RMSE, MAE and MAPE were performed to evaluate the constructed model for selecting the best prediction model. (1) From the year 2010 to 2021, the overall incidence of tuberculosis in Changde City showed a downward trend. (2) The daily TB notifications was positively correlated with average temperature (r = 0.231), maximum temperature (r = 0.194), minimum temperature (r = 0.165), sunshine duration (r = 0.329), PM2.5 (r = 0.097), PM10 (r = 0.215) and O3 (r = 0.084) (p < 0.05). However, there was a significant negative correlation between the daily TB notifications and mean air pressure (r = -0.119), precipitation (r = -0.063), relative humidity (r = -0.084), CO (r = -0.038) and SO2 (r = -0.034) (p < 0.05). (3) The random forest regression model had the best fitting effect, while the BP neural network model exhibited the best prediction. (4) The validation set of the BP neural network model, including average daily temperature, sunshine hours and PM10, showed the lowest root mean square error, mean absolute error and mean absolute percentage error, followed by support vector regression. The prediction trend of the BP neural network model, including average daily temperature, sunshine hours and PM10, successfully mimics the actual incidence, and the peak incidence highly coincides with the actual aggregation time, with a high accuracy and a minimum error. Taken together, these data suggest that the BP neural network model can predict the incidence trend of tuberculosis in Changde City.

Consistency of Seasonal Mean and Extreme Precipitation Projections Over Europe Across a Range of Climate Model Ensembles.

Uncertainties of regional precipitation projections are substantial, and users of such projections face the so-called practitioners dilemma: a plethora of projections with different models from different ensembles of different types and generations are available. But the consistency of these projections has not been systematically assessed, such that no clear guidance about the use of these ensembles exists. Therefore, we systematically compare, separately for each season, projections of mean precipitation and extremes of daily precipitation over Europe across a wide range of climate model ensembles. We specifically consider the global climate model ensembles CMIP3, CMIP5, Coupled Model Intercomparison Project Phase 6 (CMIP6), and HighresMIP as well as the regional climate model ensembles ENSEMBLES and EURO-CORDEX. All considered ensembles agree in their large-scale patterns of changes for both mean and extreme daily precipitation, but at the regional scale, substantial discrepancies and inconsistencies are evident. Within and across ensemble spread is strongest in summer, in particular for the drying trend over the Mediterranean and Eastern Europe. CMIP5 and CMIP6 are broadly consistent. The regional climate model (RCM) ensembles modify the signals of the driving global climate models indicating potential added value. The high resolution of the RCM and HighresMIP ensembles seems to be key over the Alps for summer precipitation. Our study provides important information for users of climate projections as it helps to establish continuity across generations and types of climate models, and aids the design of new climate impact studies.

Projected Changes of Day-to-Day Precipitation and Choco Low-Level Jet Relationships over the Far Eastern Tropical Pacific and Western Colombia from Two CMIP6 GCM Models

The far Eastern Tropical Pacific (EPAC) and Western Colombia are one of the rainiest places on Earth, and the Choco low-level jet (ChocoJet) is one of the processes that influence the formation of copious precipitation and convection organization in this region. This study investigates the projected changes in precipitation in this region using historical and future simulations based on model output from two models contributing to the Coupled Model Intercomparison Project phase 6 (CMIP6). In close agreement with observations, models simulate that ChocoJet intensity is directly proportional to precipitation in the region. This relationship is also found far inland in Central America, the northwestern part of South America Pacific Coast, and the intermountain valleys of the Colombian Andes. Late 21st century simulations show a southward migration in mean and regional daily precipitation consistent with a decreased ChocoJet intensity. The weaker ChocoJet is related to a projected increase in EPAC tropical sea surface temperatures (SSTs) and an increased frequency and intensity of the warm phase of the Niño 1+2 SST interannual variability.

Exploring the association between precipitation and population cases of ocular toxoplasmosis in Colombia.

Previous studies suggest a relationship between precipitation and ocular toxoplasmosis (OT) reactivation and congenital toxoplasmosis infection. We aimed to investigate the relationship between precipitation and the frequency of new OT cases in Colombia from 2015 to 2019. This retrospective cohort study analyzed data obtained from a claims-based database created by the Colombian Ministry of Health and national registries of precipitation of the Institute of Hydrology, Meteorology, and Environmental Studies. We estimated the daily number of OT cases, interpolating data from the average number of annual cases from 2015 to 2019. Then, we compared exposures (mean daily precipitation) in the case period in which the events (interpolated OT new cases) occurred by a quasi-Poisson regression, combined with a distributed lag non-linear model to estimate the non-linear and lag-response curve. In the 5-year analysis, there were 1,741 new OT cases. Most of the cases occurred in 2019, followed by 2015 and 2018. New OT cases among departments were significantly different (P< 0.01). The cumulative exposure-response curve was decreasing for most departments. Nevertheless, in Chocó, Bogotá, Cesar, Cauca, and Guajira, when a certain amount of precipitation accumulates, the relative risk (RR) increases, which was contrary to the pattern observed in the other regions. The response curves to the one-day lag showed that precipitation influences the RR; however, the trends vary by department. Finally, an increasing trend in the number of cases was directly proportional to precipitation in Guajira, Atlántico, Norte de Santander, Santander, Caquetá and Quindío (r = 0.84; P< 0.05). Precipitation influenced the RR for new OT cases. However, varying trends among geographical regions (departments) lead us to hypothesize that other sociodemographic, behavioral, and environmental variables, such as wind and water contamination, could influence the RR.

Spatial Patterns and Magnitudes of Agriculturally Pertinent Climate Indicators across Agroecosystems in Türkiye

Abstract This research utilized NASA–Prediction of Worldwide Energy Resource (POWER) datasets to quantify and map long-term annual and growing-season mean maximum, minimum, and mean air temperatures (Tmax, Tmin, and Tavg); diurnal temperature range (DTR); growing degree-days (thermal unit) (GDD); seasonal total precipitation; mean daily precipitation; incoming shortwave radiation Rs; relative humidity (RH); wind speed u2; saturation and actual vapor pressures (es and ea); vapor pressure deficit (VPD); grass- and alfalfa-reference evapotranspiration (ETo and ETr); and aridity index (AI) over the nine agricultural zones of Türkiye (Turkey). In addition to the latitudinal influence, the influence of continentality and oceanity effects and physiographic features were evident in the spatial patterns of all climate indicators. Tmin was the most spatially variable indicator at both scales, which can have significant impact on nighttime respiration, potentially reducing agroecosystem productivity. At the annual scale, climate indicators that showed coefficient of variation (CV) greater than 0.20 were GDD, Tavg, VPD, AI, and ea. Growing-season mean indicators with CV &gt; 0.20 were GDD, AI, VPD, total precipitation, mean daily precipitation, and ea. The Rs showed the least spatial variability at both scales. Annual-scale mean CV (0.21) was 7% greater than that the growing season (0.19). To the best of our knowledge, the analyses, information, and resources presented here are the first to quantify agricultural-zone-specific climate indicators during the growing season in Türkiye and are invaluable for decision-making on issues at the intersection of meteorology, agriculture, water resources, and hydrology.

Estimating multisite precipitation by a stepwise NHMM-VAR model considering the spatiotemporal correlations of precipitation amounts

• This study developed a stepwise NHMM-VAR multisite downscaling model. • The NHMM-VAR model performs well not only in modeling the statistical properties but also in reproducing the spatiotemporal correlations of multisite precipitation amounts. • The temporal intermittency of precipitation amounts is considered in the NHMM-VAR model. The nonhomogeneous hidden Markov model (NHMM) is a popular statistical downscaling (SD) approach that can be used for multisite precipitation estimation. However, the NHMM assumes that once the days with the occurrence of precipitation have been determined, the precipitation amounts at each station on wet days can be conditionally independently generated from a parametric family of probability distribution functions without considering impacts from the past precipitation amounts and the neighboring stations. Such assumptions may lead to underestimation of the spatiotemporal correlations (i.e., temporal autocorrelation and spatial cross-correlations) of actual multisite precipitation amounts. Thus, this study developed a stepwise downscaling method called the nonhomogeneous hidden Markov model - vector autoregressive (NHMM-VAR), in which the NHMM is used to determine the probabilities for dry days and marginal distributions of multisite daily mean precipitation amounts on wet days based on the large-scale atmospheric covariates, then a VAR(1) model is used to reproduce the spatiotemporal correlations of actual multisite daily mean precipitation amounts. A comparison experiment between the NHMM and the NHMM-VAR models is conducted against outputs from eight global climate models (GCMs) of Phase 6 of the Coupled Model Intercomparison Project (CMIP6) over the Pearl River basin (PRB) of South China. The results show that the NHMM-VAR model performs well not only in modeling the statistical properties but also in reproducing the spatiotemporal correlations of actual multisite daily mean precipitation amounts, which indicates that the VAR model does add value to the NHMM downscaling method.

Associations of Emergency Department Visits for Asthma with Precipitation and Temperature on Thunderstorm Days: A Time-Series Analysis of Data from Louisiana, USA, 2010-2012.

Background:Studies of thunderstorm asthma to understand risk factors using high-resolution climate data and asthma outcomes on a large scale are scarce. Moreover, thunderstorm asthma is not well studied in the United States.Objectives:We examined whether climate parameters involved in thunderstorms are associated with emergency department (ED) visits for acute asthma attacks in the United States.Methods:We analyzed 63,789 asthma-related, daily ED visits for all age groups, and thunderstorm-associated climate data in Louisiana during 2010 through 2012. We performed time-series analyses using quasi-Poisson regression models with natural cubic splines of date, parish, holiday, day of week, season, daily maximum concentrations of ozone () and fine particulate matter [PM in aerodynamic diameter ()], and daily mean pressure, precipitation, and temperature. Because of a significant interaction effect between temperature and lightning days on asthma-related visits, we performed stratified analyses by days with/without lightning or thunderstorm (defined by any lightning and precipitation).Results:On thunderstorm days, higher asthma-related ED visits were associated with higher daily mean precipitation [ (95% CI: 1.009, 1.300)] and lower daily mean temperature [ per 1°C change (1.000–1.021)] without carry-over effect to the next non-thunderstorm day. These higher risks were found mainly among children and adults of age. We observed similar results on lightning days. However, we did not find similar associations for non-thunderstorm or non-lightning days. Daily maximum and levels were not significantly associated with asthma ED visits on thunderstorm days.Discussion:Higher precipitation and lower temperature on thunderstorm days appear to contribute to asthma attacks among people with asthma, suggesting they should consider taking precautions during thunderstorms. EDs should consider preparing for a potential increase of asthma-related visits and ensuring sufficient stock of emergency medication and supplies for forecasted severe thunderstorm days. https://doi.org/10.1289/EHP10440