National Climatic Data Center Research Articles

Monsoon Marauders and Summer Violence: Exploring the Spatial Relationship between Climate Change and Terrorist Activity in India

Climate change is a global phenomenon that has been associated with a growing list of concerns in society today, often leaving more questions than answers. Thus, it is no surprise that questions are forming regarding the effects of climate change on global security, and more specifically, terrorism. India is the ideal case study for investigating the relationship between climate change and extremism, with average temperatures in the country reaching record highs as well as having 9,096 terrorist incidents occur during our 20-year study period between 1998 and 2017. Using daily temperature, precipitation, elevation, and distance to the equator data from the National Climatic Data Center and terrorist incidents from the Global Terrorism Data base (GTD), this study assesses the spatial relationship between these factors through geospatial analyses. Suitability analyses indicate that all the climatological variables tested—temperature, precipitation, and elevation—relate to shifting patterns of terrorist activity. We also found that beyond intensity, seasons result in a shifting of patterns in terrorist behavior to other locales Implications for the global community and for India specifically are discussed.

Collection of Weather Data from Authentic Websites and Secondary Data Sources for Rainfall Prediction

The field of data mining and machine learning has been grown many folds from the last two decades. Almost every other problem can be solved using data mining and this becomes the most tempting part of it for the scientist and researchers all over the world. Data mining can be viewed as a process of discovering knowledge. This discovery of knowledge starts with the collection of data and ends with the acquired knowledge in the form of patterns. Data collection lays the foundation for the process of knowledge discovery. In this paper, various secondary data sources from where data can be collected for rainfall prediction are deeply studied and analyzed. Some of these authentic websites and secondary data sources are NCDC (National climate data center), Kaggle, Datahub.io, UCI machine learning repository, Earth Data etc. The data collected from these secondary data sources for rainfall prediction have been critically analyzed and compared on the parameters of Accuracy, Completeness, reliability, relevance, and timeliness.

FlowDyn: A daily streamflow prediction pipeline for dynamical deep neural network applications

This paper presents a dynamical neural network framework to understand how catchment systems respond to daily rainfall-runoff processes over time. We developed an interactive Python-based deep neural network (DNN) package called FlowDyn (presented through a JS-based web platform) to simulate and forecast daily streamflow data for >180 gauging stations across the globe. Several DNN models, including long short-term memory (LSTM), Gated Recurrent Unit (GRU), and a hybrid network of convolutional neural network and LSTM (ConvLSTM), as well as an auto encoder (AE) model were developed and integrated into the FlowDyn pipeline to analyze and forecast sequential daily streamflow values that are embedded within a web-based application for demonstration and visualization. Inputs were gathered from different web services, including the catchment attributes and meteorology for large-sample studies (CAMELS), the national climatic data center (NCDC), and the global runoff data center (GRDC). DNN configurations were trained and tested with an average accuracy rating of 0.83 across 183 river basins globally. FlowDyn simulation and performance demonstrated that different DNN models were able to learn both regionally consistent and location-specific hydrological behaviors. Through the findings of this paper, we advocate the merit of applying FlowDyn package in the field of daily rainfall-runoff prediction at both local and global scales.

Insight into historical and future spring snow cover from satellite observation and model simulations over the Northern Hemisphere

AbstractAssessment of spring snow cover fraction (SCF) can be valuable for understanding the efficacy of certain Earth system models (ESMs) in simulating the energy exchange between land and atmosphere system, global hydrological cycle and future climate impacts. Here, we provide a comprehensive evaluation of simulated spring SCF from 23 and 20 ESMs participating CMIP5 and CMIP6, against satellite data from National Oceanic and Atmospheric Administration and National Climatic Data Center (NOAA/NCDC) and a long‐term Northern Hemisphere daily 5‐km snow cover extent product (JASMES) over the Northern Hemisphere (NH) and its 13 subregions during the period of 1982–2005 and 2072–2095. Our results show that (a) no model performs consistently better in simulating spring SCF from all aspects (mean annual, long‐term trend and climatological monthly of spring SCF); (b) the mean annual of spring SCF that was simulated by CMIP5 and CMIP6 model simulations was underestimated in most of the NH and overestimated over the Tibetan Plateau and eastern Asia; (c) most of the model simulations showed a stronger reduction trend for mean annual of spring SCF; (d) the climatological monthly variation of spring SCF is reasonably captured by all model simulations, except for overestimation over the TIB and EAS regions, and there is underestimation in other regions; (e) compared to CMIP5, most of the model simulations in CMIP6 exhibit an improved ability to simulate spring SCF across all aspects; (f) we confirm that the multi‐model ensemble mean (MME) is a better way to represent the three aspects of spring SCF than most individual model simulations. Finally, the spring SCF values predicted by models with better simulation abilities over the NH and its 13 subregions under different scenarios show decreasing trends. Specifically, the highest decreasing trend of spring SCF was found under high emission scenarios (RCP8.5 and SSP5‐8.5).

Simulation of Neighborhood‐Scale Air Quality With Two‐Way Coupled WRF‐CMAQ Over Southern Lake Michigan‐Chicago Region

AbstractThe southern Lake Michigan region of the United States, home to Chicago, Milwaukee, and other densely populated Midwestern cities, frequently experiences high pollutant episodes with unevenly distributed exposure and health burdens. Using the two‐way coupled Weather Research Forecast and Community Multiscale Air Quality Model (WRF‐CMAQ), we investigate criteria pollutants over a southern Lake Michigan domain using 1.3 and 4 km resolution hindcast simulations. We assess WRF‐CMAQ's performance using data from the National Climatic Data Center and Environmental Protection Agency Air Quality System. Our 1.3 km simulation slightly improves on the 4 km simulation's meteorological and chemical performance while also resolving key details in areas of high exposure and impact, that is, urban environments. At 1.3 km, we find that most air quality‐relevant meteorological components of WRF‐CMAQ perform at or above community benchmarks. WRF‐CMAQ's chemical performance also largely meets community standards, with substantial nuance depending on the performance metric and component assessed. For example, hourly simulated NO2 and O3 are highly correlated with observations (r > 0.6) while PM2.5 is less so (r = 0.4). Similarly, hourly simulated NO2 and PM2.5 have low biases (<10%), whereas O3 biases are larger (>30%). Simulated spatial pollutant patterns show distinct urban‐rural footprints, with urban NO2 and PM2.5 20%–60% higher than rural, and urban O3 6% lower. We use our 1.3 km simulations to resolve high‐pollution areas within individual urban neighborhoods and characterize seasonal changes in O3 regimes across tight spatial gradients. Our findings demonstrate both the benefits and limitations of high‐resolution simulations, particularly over urban settings.

Weather Patterns and the Frequency of Ophthalmology Consultations in the Emergency Setting

ABSTRACT Purpose To determine if weather patterns are associated with the frequency of ophthalmology consultations in the Emergency Room (ER) and trauma settings. Methods Hospital-based ophthalmology consultations between January 1, 2015 and December 31, 2020 at the University of Maryland Medical Center (UMMC) were included in the BALCITE (BALtimore Consultation, Inpatient, and Trauma of the Eye) database. Encounters were filtered to the general ER, the R. Crowley Adams Shock Trauma Center, and consultations within 24 h of admission where a delay was attributed to bed assignment. Weather data from the National Climatic Data Center (NCDC) for Baltimore, Maryland, was matched to the day of the initial encounter. Results A total of 3,877 patients were included. Overall, there was a significant association between increasing daily average temperature (R 2 = .152, p = 0.0003) and daily maximum temperature (R 2 = .243, p < 0.001) with incidence of ophthalmology consultations. There was no significant decrease in consultations with increasing precipitation (R 2 = −.007, p = 0.7477). For trauma-related consultations, there was not a significant association between consultation incidence and average daily temperature (R 2 = .011, p = 0.2013), maximum daily temperature (R 2 = −0.012, p = 0.6529), or precipitation levels (R 2 = .075, p = 0.24). The months with the highest consultation volume were September, August, and July, and the lowest numbers in April, March, and February. Conclusions Emergency ophthalmology consultations increased with increasing daily temperature. Trauma-specific consultations did not show a significant association with weather patterns. This information may prove useful to ER providers and ophthalmologists in order to help predict frequency of consultations and better deploy personnel and resources.

Data-driven approach for robust flood prediction

Time-series prediction problems have been effectively solved by deep neural networks lately given their ability to understand temporal characteristics found in time series. In this study, a deep learning-based flood occurrence prediction method is presented for the successful interpretation of weather events and meteorological data with higher accuracy. The proposed model is evaluated on the United States National Climate Data Center (NCDC) dataset, NCDC storm events. Correlation analysis was performed on the meteorological and weather phenomenal parameters for choosing the appropriate parameters. The experimental results show that the model achieves 87.8% accuracy while predicting floods in the United States from the year 2013 to 2019.

AI‐based runoff simulation based on remote sensing observations: A case study of two river basins in the United States and Canada

AbstractData‐driven techniques are used extensively for hydrologic time‐series prediction. We created various data‐driven models (DDMs) based on machine learning: long short‐term memory (LSTM), support vector regression (SVR), extreme learning machines, and an artificial neural network with backpropagation, to define the optimal approach to predicting streamflow time series in the Carson River (California, USA) and Montmorency (Canada) catchments. The moderate resolution imaging spectroradiometer (MODIS) snow‐coverage dataset was applied to improve the streamflow estimate. In addition to the DDMs, the conceptual snowmelt runoff model was applied to simulate and forecast daily streamflow. The four main predictor variables, namely snow‐coverage (S‐C), precipitation (P), maximum temperature (Tmax), and minimum temperature (Tmin), and their corresponding values for each river basin, were obtained from National Climatic Data Center and National Snow and Ice Data Center to develop the model. The most relevant predictor variable was chosen using the support vector machine‐recursive feature elimination feature selection approach. The results show that incorporating the MODIS snow‐coverage dataset improves the models' prediction accuracies in the snowmelt‐dominated basin. SVR and LSTM exhibited the best performances (root mean square error = 8.63 and 9.80) using monthly and daily snowmelt time series, respectively. In summary, machine learning is a reliable method to forecast runoff as it can be employed in global climate forecasts that require high‐volume data processing.

Influence of Weather Patterns and Changes on Sickle Cell Disease Vaso-Occlusive Episodes and Acute Chest Syndrome: A Nationwide Sample Analysis

Introduction: Vaso-occlusive episodes (VOE) in sickle cell disease (SCD) contribute to significant morbidity and healthcare costs due to repeated emergency department (ED) visits and hospital stays. Recurrent VOE's also often trigger serious sequelae, such as acute chest syndrome (ACS). Known risk factors for VOE include dehydration and infection. The effect of weather on the incidence of VOE has been studied in specific geographic locations with inconsistent results. Our study aims to determine the correlation between change in weather patterns and VOE nationwide using a large dataset. Methods: After IRB approval, the Pediatric Health Information System (PHIS) database was queried for SCD patients younger than 21 years of age evaluated in the ED or admitted for VOE and ACS from 2016-2021. The PHIS is a comparative database with clinical and resource utilization data for inpatient, ambulatory surgery, ED, and observation unit encounters for more than 49 children's hospitals nationwide. Patients with type HbSS, HbSC, Hb Sβ+, and Sβ(0) SCD were included. Demographic and clinical information was collected, including age, sex, race, geographic location, and inpatient length of stay. Weather information was collected via the National Climatic Data Center database. The weather variables included average temperature (Tavg), delta in temperature (TΔ), average precipitation, and average wind speed for every city in which a pediatric hospital within the PHIS database is located. Descriptive statistics were used to describe the demographics and weather characteristics. Population-averaged GEE analysis with negative binomial distribution with log link was performed with weekly counts of VOE admissions as the outcome, weather parameters as predictors, and hospitals as clusters. The weather parameters used were the averages of the daily units by week. Results from the regression analyses were reported as incidence rate ratios with 95% confidence intervals. A two-sided p-value less than 0.05 was considered as statistically significant for the analysis. Results: Of the total 79, 872 encounters included, 86% were diagnosed with VOE and 14% with ACS. Approximately, 52% of patients presented to hospitals in the southern region (Table 1). Regions (number of participating hospitals) include midwest (MW, 8), northeast (NE, 4), south (S, 11), and west (W, 5). Information from both the PHIS and weather database were available for 11,547 weeks. The south had the highest Tavg, TΔ, and precipitation. The MW had the highest wind speed and lowest Tavg. The NE had the lowest TΔ and Tavg. The west had the lowest wind speed and precipitation. The weather variables remained non-significant for ACS admissions. Overall, Tavg, TΔ, and wind speed were significant for VOE admissions while Tavg, precipitation, and wind speed were significant for VOE ED visits. Table 2 highlights the associations between overall VOE visits, weather variables, and regions. Conclusion: Our results show average temperature and delta in temperature (TΔ) is associated with the incidence of VOE admissions and ED visits. A positive association is observed in the midwest and northeast region for Tavg and negative association in the northeast region for TΔ. Future prospective studies will need to assess the effect of weather on VOE in a larger population, so specific weather-related guidelines can be developed for SCD patients and families. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Impacts of Climatic Variability on Agricultural Total Factor Productivity Growth in the Southern United States

This study investigates the impact of climatic variability on both agricultural production and total factor productivity (TFP) in the Southern United States (US). It also aims at identifying the drivers of productivity in this region. The analysis is tailored to inform decision makers about effective policy options to increase and sustain productivity in this important agricultural region. We use data from the US Department of Agriculture, National Climatic Data Center, and US Geological Survey to estimate alternative stochastic production frontier models. The estimated parameters are then analyzed and used to compute and decompose TFP into several measures of efficiency. The results show that agricultural production in the Southern US is more responsive to labor and has been increasing at a 1.13 percent rate annually. The findings also suggest that while precipitation, on average, has a positive and significant impact on productivity, intra-annual variation in both temperature and precipitation, which can be considered as anomalies, has a negative and significant impact on production. The impact of climatic effects on productivity across states is mixed and technological progress has been the main driver of TFP growth. Findings indicate that climatic variability is having a negative impact on agricultural productivity in the Southern US, similar in magnitude to the positive impact of irrigation.

Climate rather than economic variables might have caused increase in US homicide but not suicide rates during the Great Depression

BackgroundIt is widely believed that during the Great Depression (1929–1933) there was a rise in suicidal rates which was causally related to the increase in unemployment. There are no studies on the effect the Great Depression had on homicidal rates MethodsThe data concerning suicide, homicide, economic and climatic variables for the years 1900–1940 for the whole of the US were gathered from the US Center for Disease Control, the Maddison Project, the National Bureau of Economic Research and the National Climatic Data Center. Time Series Analysis was performed. ResultsThe results are inconclusive on the role of economic factors but preclude any role of climate on suicidal rates during the years 1900–1940 in the US. Suicidal rates might have a 24-years periodicity, however much longer time series are needed to confirm this. On the contrary they strongly suggest an effect of higher temperatures on homicidal rates after 1922. ConclusionsThe results of the current study suggest a direct and clear effect of climate (higher temperatures) on the increasing homicidal rates in the US after 1922 but failed to establish a causal relationship between suicide rates and economic or climate variables. These should be considered together with increasing concerns on the possible effect of climate change on mental health

A comparative study of two-way and offline coupled WRF v3.4 and CMAQ v5.0.2 over the contiguous US: performance evaluation and impacts of chemistry-meteorology feedbacks on air quality.

The two-way coupled Weather Research and Forecasting and Community Multiscale Air Quality (WRF-CMAQ) model has been developed to more realistically represent the atmosphere by accounting for complex chemistry-meteorology feedbacks. In this study, we present a comparative analysis of two-way (with consideration of both aerosol direct and indirect effects) and offline coupled WRF v3.4 and CMAQ v5.0.2 over the contiguous US. Long-term (5 years from 2008 to 2012) simulations using WRF-CMAQ with both offline and two-way coupling modes are carried out with anthropogenic emissions based on multiple years of the U.S. National Emission Inventory and chemical initial and boundary conditions derived from an advanced Earth system model (i.e., a modified version of the Community Earth System Model/Community Atmospheric Model). The comprehensive model evaluations show that both two-way WRF-CMAQ and WRF-only simulations perform well for major meteorological variables such as temperature at 2 m, relative humidity at 2 m, wind speed at 10 m, precipitation (except for against the National Climatic Data Center data), and shortwave and longwave radiation. Both two-way and offline CMAQ also show good performance for ozone (O3) and fine particulate matter (PM2.5). Due to the consideration of aerosol direct and indirect effects, two-way WRF-CMAQ shows improved performance over offline coupled WRF and CMAQ in terms of spatiotemporal distributions and statistics, especially for radiation, cloud forcing, O3, sulfate, nitrate, ammonium, elemental carbon, tropospheric O3 residual, and column nitrogen dioxide (NO2). For example, the mean biases have been reduced by more than 10 W m-2 for shortwave radiation and cloud radiative forcing and by more than 2 ppb for max 8 h O3. However, relatively large biases still exist for cloud predictions, some PM2.5 species, and PM10 that warrant follow-up studies to better understand those issues. The impacts of chemistry-meteorological feedbacks are found to play important roles in affecting regional air quality in the US by reducing domain-average concentrations of carbon monoxide (CO), O3, nitrogen oxide (NO x ), volatile organic compounds (VOCs), and PM2.5 by 3.1% (up to 27.8%), 4.2% (up to 16.2%), 6.6% (up to 50.9%), 5.8% (up to 46.6%), and 8.6% (up to 49.1%), respectively, mainly due to reduced radiation, temperature, and wind speed. The overall performance of the two-way coupled WRF-CMAQ model achieved in this work is generally good or satisfactory and the improved performance for two-way coupled WRF-CMAQ should be considered along with other factors in developing future model applications to inform policy making.

Case Study in Forensic Meteorology: Investigating the 3 April 1956 Tornadoes in Western Lower Michigan

An investigation into a historic tornado outbreak in western Lower Michigan yielded several significant findings and provided details about path length, duration, and intensity of each tornado. These findings are presented here, along with discrepancies that were found with the National Climatic Data Center’s storm event database for the number of tornadoes and fatalities that occurred in this event. Potential strategies in forensic meteorology for future studies of historic tornado events also are presented, including suggestions for gathering and synthesizing data. Finally, strategies are discussed for using anniversary events to increase public awareness of severe weather.

Climate change, extreme events, and increased risk of salmonellosis: foodborne diseases active surveillance network (FoodNet), 2004-2014

BackgroundInfections with nontyphoidal Salmonella cause an estimated 19,336 hospitalizations each year in the United States. Sources of infection can vary by state and include animal and plant-based foods, as well as environmental reservoirs. Several studies have recognized the importance of increased ambient temperature and precipitation in the spread and persistence of Salmonella in soil and food. However, the impact of extreme weather events on Salmonella infection rates among the most prevalent serovars, has not been fully evaluated across distinct U.S. regions.MethodsTo address this knowledge gap, we obtained Salmonella case data for S. Enteriditis, S. Typhimurium, S. Newport, and S. Javiana (2004-2014; n = 32,951) from the Foodborne Diseases Active Surveillance Network (FoodNet), and weather data from the National Climatic Data Center (1960-2014). Extreme heat and precipitation events for the study period (2004-2014) were identified using location and calendar day specific 95th percentile thresholds derived using a 30-year baseline (1960-1989). Negative binomial generalized estimating equations were used to evaluate the association between exposure to extreme events and salmonellosis rates.ResultsWe observed that extreme heat exposure was associated with increased rates of infection with S. Newport in Maryland (Incidence Rate Ratio (IRR): 1.07, 95% Confidence Interval (CI): 1.01, 1.14), and Tennessee (IRR: 1.06, 95% CI: 1.04, 1.09), both FoodNet sites with high densities of animal feeding operations (e.g., broiler chickens and cattle). Extreme precipitation events were also associated with increased rates of S. Javiana infections, by 22% in Connecticut (IRR: 1.22, 95% CI: 1.10, 1.35) and by 5% in Georgia (IRR: 1.05, 95% CI: 1.01, 1.08), respectively. In addition, there was an 11% (IRR: 1.11, 95% CI: 1.04-1.18) increased rate of S. Newport infections in Maryland associated with extreme precipitation events.ConclusionsOverall, our study suggests a stronger association between extreme precipitation events, compared to extreme heat, and salmonellosis across multiple U.S. regions. In addition, the rates of infection with Salmonella serovars that persist in environmental or plant-based reservoirs, such as S. Javiana and S. Newport, appear to be of particular significance regarding increased heat and rainfall events.

Polyphony of Short-Term Climatic Variations

It is widely accepted to believe that humanity is mainly responsible for the worldwide temperature growth during the period of instrumental meteorological observations. This paper aims to demonstrate that it is not so simple. Using a wavelet analysis on the example of the time series of the global mean near-surface air temperature created at the American National Climate Data Center (NCDC), some complex structures of inter-annual to multidecadal global mean temperature variations were discovered. The origin of which seems to be better attributable to the Chandler wobble in the Earth’s Pole motion, the Luni-Solar nutation, and the solar activity cycles. Each of these external forces is individually known to climatologists. However, it is demonstrated for the first time that responses of the climate system to these external forces in their integrity form a kind of polyphony superimposed on a general warming trend. Certainly, the general warming trend as such remains to be unconsidered. However, its role is not very essential in the timescale of a few decades. Therefore, it is this polyphony that will determine climate evolution in the nearest future, i.e., during the time most important for humanity currently.

Estimating basis functions in massive fields under the spatial mixed effects model

AbstractSpatial prediction is commonly achieved under the assumption of a Gaussian random field by obtaining maximum likelihood estimates of parameters, and then using the kriging equations to arrive at predicted values. For massive datasets, fixed rank kriging using the expectation–maximization algorithm for estimation has been proposed as an alternative to the usual but computationally prohibitive kriging method. The method reduces computation cost of estimation by redefining the spatial process as a linear combination of basis functions and spatial random effects. A disadvantage of this method is that it imposes constraints on the relationship between the observed locations and the knots. We develop an alternative method that utilizes the spatial mixed effects model, but allows for additional flexibility by estimating the range of the spatial dependence between the observations and the knots via an alternating expectation conditional maximization algorithm. Experiments show that our methodology improves estimation without sacrificing prediction accuracy while also minimizing the additional computational burden of extra parameter estimation. The methodology is applied to a temperature dataset archived by the United States National Climate Data Center, with improved results over previous methodology.

Ensemble generation for hurricane hazard assessment along the United States’ Atlantic coast

Atlantic tropical cyclones (TCs) affect the United States every year, and depending on their intensity, can cause coastal storm surge and inland inundation, leading to substantial damages in the east coast. Accurate TC risk assessment, however, is not easily quantified given the limited spatial and temporal historical records. This paper describes the development of a model framework for TC forecasts based on large ensemble generation. We illustrate the capability of the ensemble technique to improve TC forecasts by generating probabilistic forecasts of track and intensity. We show that the using this approach can generate tracks that are statistically similar to those of the underlying historical records, even TCs with long return periods. We began with historical compilations of the NOAA National Climatic Data Center (NCDC) tropical cyclone database. TCs reaching a hurricane strength and making landfall in or passing close to the United States were identified. The geographical area influenced by these hurricanes was discretized and the parameters of Markov chains and multivariate distributions were derived for each discretized area. Synthetic tracks were generated using repetitive random draws from the spatiotemporal distribution of historical genesis and storm motion, conditioned by Markov chains for each 6-hour displacement. Kolmogorov–Smirnov (K–S) tests revealed that the ensemble members and ’best track’ data are drawn from the same underlying population at the 95% significance level, with no test statistics exceeding the critical value of 0.13. The proposed algorithm is validated in both macro and micro scales. The results revealed that the general pattern of hurricane activities conforms well to the historical observations with high spatial correlations, over 90% for the Saffir–Simpson hurricane categories of 1 through 4 and near 80% for the category 5. The track generator also produces a history of potential wind and translational speeds for both of these scales.

Effect of meteorological factors and air pollutants on fractures: a nationwide population-based ecological study

ObjectiveTo determine the association of meteorological factors and air pollutants (MFAPs) with fracture and to estimate the effect size/time lag.DesignThis is a nationwide population-based ecological study from 2008 to 2017.SettingEight...

Differences in the Diurnal Variation of Precipitation Estimated by Spaceborne Radar, Passive Microwave Radiometer, and IMERG

AbstractAccurate, high‐resolution measurements of the precipitation diurnal cycle are important for understanding local variations in precipitation and the underlying processes which cause them. Combining 16 years of measurements from the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) and 4 years from the Global Precipitation Measurement (GPM) Dual‐frequency Precipitation Radar (DPR) creates a 20‐year mean of near surface precipitation rate at 0.1° × 0.1°, hourly resolution from 35°N to 35°S. Similar data sets are created using the same proportions of the TRMM Microwave Imager and GPM Microwave Imager measurements, and 18 years of the Integrated Multi‐satellitE Retrievals for GPM (IMERG) precipitation product. The phase and amplitude of the diurnal variation from each data set are calculated at each grid point using a fast Fourier transform (FFT). The satellite data are validated with 14 years of hourly National Climatic Data Center rain gauge measurements over the southeast United States by applying the same FFT method. Spaceborne radar best represents the unaveraged magnitude and diurnal phase measured by the gauges in this region. Time delays are found in precipitation retrievals from passive microwave observations as well as in the IMERG product. The strengths and weaknesses of these high‐resolution data sets in determining the diurnal cycle of precipitation on a climatological scale are discussed across the tropics and subtropics. In general, when compared to PR and DPR retrievals, IMERG overestimates the global precipitation and has varying time lags, which tend to be larger and earlier over regions where organized convective systems are common.

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

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.