The majority of the wettest cities in the United States are located in the Southeast. The major city with the most precipitation is New Orleans, Louisiana, which receives an average of 1592 millimeters (62.7 inches) of precipitation every year, based on an average between 1981 and 2010.

This statistic shows the ten major U.S. cities with the most rainy days per year between 1981 and 2010. Rochester, New York, had an average of about 167 days per year with precipitation. The sunniest city in the U.S. was Phoenix, Arizona, with an average of 85 percent of sunshine per day.

Historical changes of annual temperature and precipitation indices at selected 210 U.S. cities

This dataset provide:

Annual average temperature, total precipitation, and temperature and precipitation extremes calculations for 210 U.S. cities.

Historical rates of changes in annual temperature, precipitation, and the selected temperature and precipitation extreme indices in the 210 U.S. cities.

Estimated thresholds (reference levels) for the calculations of annual extreme indices including warm and cold days, warm and cold nights, and precipitation amount from very wet days in the 210 cities.

Annual average of daily mean temperature, Tmax, and Tmin are included for annual average temperature calculations. Calculations were based on the compiled daily temperature and precipitation records at individual cities.

Temperature and precipitation extreme indices include: warmest daily Tmax and Tmin, coldest daily Tmax and Tmin , warm days and nights, cold days and nights, maximum 1-day precipitation, maximum consecutive 5-day precipitation, precipitation amounts from very wet days.

Number of missing daily Tmax, Tmin, and precipitation values are included for each city.

Rates of change were calculated using linear regression, with some climate indices applied with the Box-Cox transformation prior to the linear regression.

The historical observations from ACIS belong to Global Historical Climatological Network - daily (GHCN-D) datasets. The included stations were based on NRCC’s “ThreadEx” project, which combined daily temperature and precipitation extremes at 255 NOAA Local Climatological Locations, representing all large and medium size cities in U.S. (See Owen et al. (2006) Accessing NOAA Daily Temperature and Precipitation Extremes Based on Combined/Threaded Station Records).

Resources:

See included README file for more information.

Additional technical details and analyses can be found in: Lai, Y., & Dzombak, D. A. (2019). Use of historical data to assess regional climate change. Journal of climate, 32(14), 4299-4320. https://doi.org/10.1175/JCLI-D-18-0630.1

Other datasets from the same project can be accessed at: https://kilthub.cmu.edu/projects/Use_of_historical_data_to_assess_regional_climate_change/61538

ACIS database for historical observations: http://scacis.rcc-acis.org/

GHCN-D datasets can also be accessed at: https://www.ncei.noaa.gov/data/global-historical-climatology-network-daily/

Station information for each city can be accessed at: http://threadex.rcc-acis.org/

• 2024 August updated -

  Annual calculations for 2022 and 2023 were added.

  Linear regression results and thresholds for extremes were updated because of the addition of 2022 and 2023 data.

  Note that future updates may be infrequent.

• 2022 January updated -

  Annual calculations for 2021 were added.

  Linear regression results and thresholds for extremes were updated because of the addition of 2021 data.

• 2021 January updated -

  Annual calculations for 2020 were added.

  Linear regression results and thresholds for extremes were updated because of the addition of 2020 data.

• 2020 January updated -

  Annual calculations for 2019 were added.

  Linear regression results and thresholds for extremes were updated because of the addition of 2019 data.

  Thresholds for all 210 cities were combined into one single file – Thresholds.csv.

• 2019 June updated -

  Baltimore was updated with the 2018 data (previously version shows NA for 2018) and new ID to reflect the GCHN ID of Baltimore-Washington International AP. city_info file was updated accordingly.

  README file was updated to reflect the use of "wet days" index in this study. The 95% thresholds for calculation of wet days utilized all daily precipitation data from the reference period and can be different from the same index from some other studies, where only days with at least 1 mm of precipitation were utilized to calculate the thresholds. Thus the thresholds in this study can be lower than the ones that would've be calculated from the 95% percentiles from wet days (i.e., with at least 1 mm of precipitation).

The majority of the wettest cities in the United States are located in the Southeast. The major city with the most precipitation is New Orleans, Louisiana, which receives an average of 1592 millimeters (62.7 inches) of precipitation every year, based on an average between 1981 and 2010.

In 2024, Louisiana recorded ***** inches of precipitation. This was the highest precipitation within the 48 contiguous U.S. states that year. On the other hand, Nevada was the driest state, with only **** inches of precipitation recorded. Precipitation across the United States Not only did Louisiana record the largest precipitation volume in 2024, but it also registered the highest precipitation anomaly that year, around 14.36 inches above the 1901-2000 annual average. In fact, over the last decade, rainfall across the United States was generally higher than the average recorded for the 20th century. Meanwhile, the driest states were located in the country's southwestern region, an area which – according to experts – will become even drier and warmer in the future. How does global warming affect precipitation patterns? Rising temperatures on Earth lead to increased evaporation which – ultimately – results in more precipitation. Since 1900, the volume of precipitation in the United States has increased at an average rate of **** inches per decade. Nevertheless, the effects of climate change on precipitation can vary depending on the location. For instance, climate change can alter wind patterns and ocean currents, causing certain areas to experience reduced precipitation. Furthermore, even if precipitation increases, it does not necessarily increase the water availability for human consumption, which might eventually lead to drought conditions.

Brevard (North Carolina) recorded the wettest precipitation anomaly in 2019 with around ** inches of precipitation more than average. Wet years were seen in cities that were hit by heavy rain events such as tropical storms.

The purpose of this tool is to estimate daily precipitation patterns for a yearly cycle at any location on the globe. The user input is simply the latitude and longitude of the selected location. There is an embedded Zip Code search routine to find the latitude and longitude for US cities. GlobalRainSIM forecasts the daily rainfall based upon two databases.The first was the average number of days in a month with precipitation (wet days) that were compiled and interpolated by Legates and Willmott (1990a and 1990b) with further improvements by Willmott and Matsuura (1995). The second database was the global average monthly precipitation data collected 1961-1990 and cross-validated by New et al. (1999). These two datasets were then used to establish the monthly precipitation totals and the frequency of precipitation in a month. The average precipitation event was calculated as the monthly mean divided by the number of wet days. This mean value was then randomly assigned to a day of the month looping through the number of wet days. In other words, if the average monthly rainfall was 10 mm/month with 5 average wet days, each rain event was 2 mm. This amount (2 mm) was then randomly assigned to 5 days of that month. The advantage of this tool is that a typical pattern of precipitation can be simulated for any global location arriving at an •average year• as a baseline case for comparison. This tool also outputs the daily rainfall as a file or can be easily embedded within another program. Resources in this dataset:Resource Title: Global RainSIM Verson 1.0. File Name: Web Page, url: https://www.ars.usda.gov/research/software/download/?softwareid=227&modecode=50-60-05-00 download page

Extreme rainfall events can lead to catastrophic consequences such as widespread property damage or loss of lives. In engineering design, we take into account different characteristics of extreme events, such as average recurrence interval, duration and volume of rainfall delivered. We have so far relied on historical rainfall observations to estimate these characteristics, however, there is evidence that rainfall patterns in the future will look different from the past. Global Climate Models can be used to understand to some extent, the magnitude of these changes. In this repository, we provide updated rainfall volumes for 24-hour duration storms using 5 different downscaled climate model datasets and for selected cities in the United States. For more information on the update process, please refer to the associated publication of this dataset.

In 2024, the United States saw some **** inches of precipitation. The main forms of precipitation include hail, drizzle, rain, sleet, and snow. Since the turn of the century, 2012 was the driest year on record with an annual precipitation of **** inches. Regional disparities in rainfall Louisiana emerged as the wettest state in the U.S. in 2024, recording a staggering ***** inches (*** meters) of precipitation—nearly **** inches (ca. ** centimeters) above its historical average. In stark contrast, Nevada received only **** inches (ca. ** centimeters), underscoring the vast differences in rainfall across the nation. These extremes illustrate the uneven distribution of precipitation, with the southwestern states experiencing increasingly dry conditions that experts predict will worsen in the coming years. Drought concerns persist Drought remains a significant concern in many parts of the country. The Palmer Drought Severity Index (PDSI) for the contiguous United States stood at ***** in December 2024, indicating moderate to severe drought conditions. This reading follows three years of generally negative PDSI values, with the most extreme drought recorded in December 2023 at *****.

The Baltimore radar rainfall dataset was developed from a multi-sensor analysis combining radar rainfall estimates from the Sterling, VA WSR88D radar (KLWX) with measurements from a collection of ground based rain gages. The archived data have a 15-minute time resolution and a grid resolution of 0.01 degree latitude/longitude (approximately 1 km x 1 km); 15-minute rainfall accumulations for each grid are in mm. The dataset spans 22 years, 2000-2021, and covers an area of approximately 4,900 km^2 (70 by 70 grids, each with approximate area of 1 km^2) surrounding the Baltimore, MD metropolitan area (Figure 1). The rainfall data cover the six months from April to September of each year. This is the period with most intense sub-daily rainfall and the period for which radar measurements are most accurate. Figure 1 illustrates the climatological analyses of mean annual frequency of days with at least 1 hour rainfall exceeding 25 mm. The striking spatial variability of convective rainfall is illustrated in Figure 2 by the April-September climatology of annual lightning strikes.

As with many long-term environmental data sets, sensor technology has changed during the time period of the archive. The Sterling, VA WSR88D radar underwent a hardware upgrade from single polarization to dual polarization in 2012. Prior to the upgrade, rainfall was estimated using a conventional radar-reflectivity algorithm (HydroNEXRAD) which converts reflectivity measurements in polar coordinates from the lowest sweep to rainfall estimates on a 0.01 degree latitude-longitude grid at the surface (see Seo et al. 2010 and Smith et al. 2012 for details on the algorithm). The polarimetric upgrade introduced new measurements into the radar-rainfall algorithm. In addition to reflectivity, the operational rainfall product, Digital Precipitation Rate (DPR), directly uses differential reflectivity and specific differential phase shift measurements to estimate rainfall (https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C00708; see also Giangrande and Ryzhkov 2008). Details of the algorithm structure and parameterization for the DPR radar-rainfall estimates have been modified during the 10-year period of the data set.

A storm-based (daily) multiplicative mean field bias has been applied to both datasets. The mean field bias is computed as the ratio of daily rain gage rainfall at a point to daily radar rainfall for the bin that contains the gage. The rain gage dataset is compiled from rain gages in the Baltimore metropolitan region and surrounding areas and includes gages acquired from both Baltimore City and Baltimore County, and the Global Historical Climatology Network daily (GHCNd). Mean field bias improves rainfall estimates and diminishes the impacts of changing measurement procedures.

The dataset has been archived in 2 formats: netCDF gridded rainfall, 1 file for each 15-minute time period, and csv or excel format point rainfall (1 point at the center of each grid) in a timeseries format with 1 file per calendar month covering the entire 70x70 domain. The csv files are in folders organized by calendar year. The first five columns in each file represent year, month, day, hour, and minute and can be combined to generate a unique date-time value for each time step. Each additional column is a complete time series for the month and represents data from one of the 1-km2 grid cells in the original data set.

The latitude and longitude coordinates for each pixel in the grid are provided. The latitude and longitude represent the centroid of the cell, which is square when represented in latitude and longitude coordinates and rectangular when represented in other distance-based coordinate systems such as State Plane or Universal Transverse Mercator. There are 4900 pixels in the domain. In order to visualize the data using GIS or other software, the user needs to associate each column in the annual rainfall file with the latitude and longitude values for that grid cell number.

These data may be subject to modest revision or reformatting in future versions. The current version is version 2.0 and is being offered to users who wish to explore the data. We will revise this document as needed.

Temperature and precipitation projections for NYC reported by the New York City Panel on Climate Change (NPCC). The New York City Panel on Climate Change (NPCC) started in 2009 and was codified in Local Law 42 of 2012 with a mandate to provide an authoritative and actionable source of scientific information on future climate change and its potential impacts. The Intergovernmental Panel on Climate Change (IPCC) is the United Nations body for assessing the science related to climate change.

This data release contains the input-data files and R scripts associated with the analysis presented in [citation of manuscript]. The spatial extent of the data is the contiguous U.S. The input-data files include one comma separated value (csv) file of county-level data, and one csv file of city-level data. The county-level csv (“county_data.csv”) contains data for 3,109 counties. This data includes two measures of water use, descriptive information about each county, three grouping variables (climate region, urban class, and economic dependency), and contains 18 explanatory variables: proportion of population growth from 2000-2010, fraction of withdrawals from surface water, average daily water yield, mean annual maximum temperature from 1970-2010, 2005-2010 maximum temperature departure from the 40-year maximum, mean annual precipitation from 1970-2010, 2005-2010 mean precipitation departure from the 40-year mean, Gini income disparity index, percent of county population with at least some college education, Cook Partisan Voting Index, housing density, median household income, average number of people per household, median age of structures, percent of renters, percent of single family homes, percent apartments, and a numeric version of urban class. The city-level csv (city_data.csv) contains data for 83 cities. This data includes descriptive information for each city, water-use measures, one grouping variable (climate region), and 6 explanatory variables: type of water bill (increasing block rate, decreasing block rate, or uniform), average price of water bill, number of requirement-oriented water conservation policies, number of rebate-oriented water conservation policies, aridity index, and regional price parity. The R scripts construct fixed-effects and Bayesian Hierarchical regression models. The primary difference between these models relates to how they handle possible clustering in the observations that define unique water-use settings. Fixed-effects models address possible clustering in one of two ways. In a "fully pooled" fixed-effects model, any clustering by group is ignored, and a single, fixed estimate of the coefficient for each covariate is developed using all of the observations. Conversely, in an unpooled fixed-effects model, separate coefficient estimates are developed only using the observations in each group. A hierarchical model provides a compromise between these two extremes. Hierarchical models extend single-level regression to data with a nested structure, whereby the model parameters vary at different levels in the model, including a lower level that describes the actual data and an upper level that influences the values taken by parameters in the lower level. The county-level models were compared using the Watanabe-Akaike information criterion (WAIC) which is derived from the log pointwise predictive density of the models and can be shown to approximate out-of-sample predictive performance. All script files are intended to be used with R statistical software (R Core Team (2017). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org) and Stan probabilistic modeling software (Stan Development Team. 2017. RStan: the R interface to Stan. R package version 2.16.2. http://mc-stan.org).

Dataset Title: Urban Air Quality and Health Impact Dataset: A Comprehensive Overview of U.S. Cities

Description:

This dataset provides an extensive collection of synthetic data related to urban air quality and its potential health impacts across major U.S. cities. The data has been augmented to include a wide range of features, making it a valuable resource for research and analysis in the fields of environmental science, public health, and urban studies.

Features:

• DateTime: Timestamp of the recorded data.
• City: The U.S. city where the data was recorded (e.g., Phoenix, San Diego, New York City).
• Temp_Max: Maximum temperature for the day (°F).
• Temp_Min: Minimum temperature for the day (°F).
• Temp_Avg: Average temperature for the day (°F).
• Feels_Like_Max: Maximum "feels like" temperature for the day (°F).
• Feels_Like_Min: Minimum "feels like" temperature for the day (°F).
• Feels_Like_Avg: Average "feels like" temperature for the day (°F).
• Dew_Point: Dew point temperature (°F).
• Humidity: Relative humidity percentage.
• Precipitation: Total precipitation for the day (inches).
• Precip_Prob: Probability of precipitation (percentage).
• Precip_Cover: Coverage of precipitation (percentage).
• Precip_Type: Type of precipitation (e.g., rain, snow).
• Snow: Amount of snowfall (inches).
• Snow_Depth: Snow depth (inches).
• Wind_Gust: Maximum wind gust speed (mph).
• Wind_Speed: Average wind speed (mph).
• Wind_Direction: Wind direction (degrees).
• Pressure: Atmospheric pressure (hPa).
• Cloud_Cover: Cloud cover percentage.
• Visibility: Visibility distance (miles).
• Solar_Radiation: Solar radiation (W/m²).
• Solar_Energy: Solar energy received (kWh).
• UV_Index: UV index level.
• Severe_Risk: Risk level of severe weather (e.g., low, moderate, high).
• Sunrise: Sunrise time (HH:MM:SS).
• Sunset: Sunset time (HH:MM:SS).
• Moon_Phase: Phase of the moon (e.g., new moon, full moon).
• Conditions: General weather conditions (e.g., clear, cloudy).
• Description: Detailed description of the weather conditions.
• Icon: Weather icon representation.
• Stations: Weather stations reporting data.
• Source: Data source information.
• Temp_Range: Temperature range for the day (difference between max and min temperatures).
• Heat_Index: Heat index value for the day.
• Severity_Score: Score representing the severity of weather conditions.
• Condition_Code: Code representing specific weather conditions.
• Month: Month of the year.
• Season: Season of the year (e.g., winter, spring).
• Day_of_Week: Day of the week.
• Is_Weekend: Indicator if the day is a weekend.
• Health_Risk_Score: Score representing the potential health risk based on weather and air quality conditions.

Usage:

This dataset is intended for researchers, data scientists, and analysts interested in studying the relationships between air quality, weather conditions, and public health. It can be used for developing predictive models, conducting statistical analyses, and creating visualizations to better understand urban environmental impacts.

Source:

The data is synthesized and augmented based on real-world weather data from major U.S. cities and is intended to serve as a comprehensive resource for urban air quality and health impact studies.

Notes:

• The dataset is synthetic and has been generated to provide a broad range of scenarios for analysis.
• Ensure to validate any findings with real-world data when applying the insights to practical applications. .

In 2011, Buffalo, New York was the major city in the United States with the most partial to heavy cloud cover, with 311 days of clouds in that year. Seattle, Pittsburg, Rochester, and Cleveland rounded out the top five cities.

Buffalo’s climate

Buffalo, New York, is located on the eastern end of Lake Erie and is the origin point of the Niagara River, and its location on Lake Erie helps to regulate the city’s climate. However, between 1981 and 2010, it had an average of 167 days with more than 0.01 inches of rainfall per year, and also had an average wind speed of 11.8 miles per hour.

The second largest city in New York

Buffalo is the second largest city in New York state, with a metro area population of 1.13 million in 2017. The city is not far from Niagara Falls, which was listed as one of the most expensive summer destinations in the state of New York.

The U.S. Geological Survey (USGS), in cooperation with the city of Harrisonville, Missouri, assessed flooding of Muddy Creek resulting from varying precipitation magnitudes and durations, antecedent soil moisture conditions, and channel conditions. The precipitation scenarios were used to develop a library of flood-inundation maps that included a 3.8-mile reach of Muddy Creek and tributaries within and adjacent to the city. Hydrologic and hydraulic models of the upper Muddy Creek Basin were used to assess streamflow magnitudes associated with simulated precipitation amounts and the resulting flood-inundation conditions. The U.S. Army Corps of Engineers Hydrologic Engineering Center-Hydrologic Modeling System (HEC–HMS; version 4.4.1) was used to simulate the amount of streamflow produced from a range of rainfall events. The Hydrologic Engineering Center-River Analysis System (HEC–RAS; version 5.0.7) was then used to route streamflows and map resulting areas of flood inundation. The hydrologic and hydraulic models were calibrated to the September 28, 2019; May 27, 2021; and June 25, 2021, runoff events representing a range of antecedent moisture conditions and hydrologic responses. The calibrated HEC–HMS model was used to simulate streamflows from design rainfall events of 30-minute to 24-hour durations and ranging from a 100- to 0.1-percent annual exceedance probability. Flood-inundation maps were produced for USGS streamflow stages of 1.0 feet (ft), or near bankfull, to 4.0 ft, or a stage exceeding the 0.1-percent annual exceedance probability interval precipitation, using the HEC–RAS model. The consequence of each precipitation duration-frequency value was represented by a 0.5-ft increment inundation map based on the generated peak streamflow from that rainfall event and the corresponding stage at the Muddy Creek stage reference location. Seven scenarios were developed with the HEC–HMS hydrologic model with resulting streamflows routed in a HEC-RAS hydraulic model and these scenarios varied by antecedent soil-moisture and channel conditions. The same precipitation scenarios were used in each of the seven antecedent moisture and channel conditions and the simulation results were assigned to a flood-inundation map condition based on the generated peak flow and corresponding stage at the Muddy Creek reference location. This data release includes: 1) tables summarizing the model results including the flood-inundation map condition of each model scenario for dry (CNI; Muddy_Creek_summary_table_1_1.csv), normal (CNII; Muddy_Creek_summary_table_1_2.csv), and wet (CNIII; Muddy_Creek_summary_table_1_3.csv) antecedent soil moisture conditions (MuddyCreek_summary_tables.zip); 2) a shapefile dataset of the streamflow inundation extents at Muddy Creek reference location stages of 1.0 to 4.0 feet (MuddyCreek_inundation_extents.zip containing MudHarMO.shp); 3) a raster dataset of the streamflow depths at Muddy Creek reference location stages of 1.0 to 4.0 feet (MuddyCreek_inundation_depths.zip containing MudharMO_X.tif where X = 1,2,3,4,5,6,7 corresponding to inundation map stages of 1.0, 1.5 , 2.0, 2.5, 3.0, 3.5, 4.0 feet)); 4) tables of hydrologic and hydraulic model performance and calibration metrics, locations of continuous pressure transducers (PTs; MuddyCreek_PT_locations.zip) and high-water marks (HWMs; MuddCreek_HWM_locations.zip) used in assessment of model calibration and validation, and time series of pressure transducer data (MuddyCreek_PT_time_series.zip) found in MuddyCreek_model_performance_calibration_metrics.zip; 5) hydrologic and hydraulic model run files used in the simulation of dry hydrologic response conditions (CN_I conditions) and effects of proposed detention storage (MuddyCreek_dry_detention.zip); 6) hydrologic and hydraulic model run files used in the simulation and calibration of dry hydrologic response conditions (CN_I conditions) and current (2019) existing channel conditions (MuddyCreek_dry_existing_conditions.zip); 7) hydrologic and hydraulic model run files used in the simulation of normal hydrologic response conditions (CN_II conditions) and effects of cleaned culverts (MuddyCreek_normal_clean_culverts.zip); 8) hydrologic and hydraulic model run files used in the simulation of normal hydrologic response conditions (CN_II conditions) and effects of detention storage (MuddyCreek_normal_detention.zip); 9) hydrologic and hydraulic model run files used in the simulation and calibration of normal hydrologic response conditions (CN_II conditions) and current (2019) existing channel conditions (MuddyCreek_normal_existing_conditions.zip); 10) hydrologic and hydraulic model run files used in the simulation of wet hydrologic response conditions (CN_III conditions) and effects of proposed detention storage (MuddyCreek_wet_detention.zip); 11) hydrologic and hydraulic model run files used in the simulation and calibration of wet hydrologic response conditions (CN_III) and current (2019) existing channel conditions (MuddyCreek_wet_existing_conditions.zip). 12) Service definition files of the Muddy Creek water depths of inundated areas (MuddyCreek_Inundation_depths.sd) and Muddy Creek inundation area polygons (MuddyCreek_inundation_extents.sd) added on September 7, 2022.

These daily weather records were compiled from a subset of stations in the Global Historical Climatological Network (GHCN)-Daily dataset. A weather record is considered broken if the value exceeds the maximum (or minimum) value recorded for an eligible station. A weather record is considered tied if the value is the same as the maximum (or minimum) value recorded for an eligible station. Daily weather parameters include Highest Min/Max Temperature, Lowest Min/Max Temperature, Highest Precipitation, Highest Snowfall and Highest Snow Depth. All stations meet defined eligibility criteria. For this application, a station is defined as the complete daily weather records at a particular location, having a unique identifier in the GHCN-Daily dataset. For a station to be considered for any weather parameter, it must have a minimum of 30 years of data with more than 182 days complete in each year. This is effectively a 30-year record of service requirement, but allows for inclusion of some stations which routinely shut down during certain seasons. Small station moves, such as a move from one property to an adjacent property, may occur within a station history. However, larger moves, such as a station moving from downtown to the city airport, generally result in the commissioning of a new station identifier. This tool treats each of these histories as a different station. In this way, it does not thread the separate histories into one record for a city. Records Timescales are characterized in three ways. In order of increasing noteworthiness, they are Daily Records, Monthly Records and All Time Records. For a given station, Daily Records refers to the specific calendar day: (e.g., the value recorded on March 7th compared to every other March 7th). Monthly Records exceed all values observed within the specified month (e.g., the value recorded on March 7th compared to all values recorded in every March). All-Time Records exceed the record of all observations, for any date, in a station's period of record. The Date Range and Location features are used to define the time and location ranges which are of interest to the user. For example, selecting a date range of March 1, 2012 through March 15, 2012 will return a list of records broken or tied on those 15 days. The Location Category and Country menus allow the user to define the geographic extent of the records of interest. For example, selecting Oklahoma will narrow the returned list of records to those that occurred in the state of Oklahoma, USA. The number of records broken for several recent periods is summarized in the table and updated daily. Due to late-arriving data, the number of recent records is likely underrepresented in all categories, but the ratio of records (warm to cold, for example) should be a fairly strong estimate of a final outcome. There are many more precipitation stations than temperature stations, so the raw number of precipitation records will likely exceed the number of temperature records in most climatic situations.

The continued provision of water from rivers in the southwestern United States to downstream cities, natural communities and species is at risk due to higher temperatures and drought conditions in recent decades. Snowpack and snowfall levels have declined, snowmelt and peak spring flows are arriving earlier, and summer flows have declined. Concurrent to climate change and variation, a century of fire suppression has resulted in dramatic changes to forest conditions, and yet, few studies have focused on determining the degree to which changing forests have altered flows. In this study, we evaluated changes in flow, climate, and forest conditions in the Salt River in central Arizona from 1914–2012 to compare and evaluate the effects of changing forest conditions and temperatures on flows. After using linear regression models to remove the influence of precipitation and temperature, we estimated that annual flows declined by 8–29% from 1914–1963, coincident with a 2-fold increase in basal area, a 2-3-fold increase in canopy cover, and at least a 10-fold increase in forest density within ponderosa pine forests. Streamflow volumes declined by 37–56% in summer and fall months during this period. Declines in climate-adjusted flows reversed at mid-century when spring and annual flows increased by 10–31% from 1964–2012, perhaps due to more winter rainfall. Additionally, peak spring flows occurred about 12 days earlier in this period than in the previous period, coincident with winter and spring temperatures that increased by 1–2°C. While uncertainties remain, this study adds to the knowledge gained in other regions that forest change has had effects on flow that were on par with climate variability and, in the case of mid-century declines, well before the influence of anthropogenic warming. Current large-scale forest restoration projects hold some promise of recovering seasonal flows.

Precipitation Sensor Market Outlook

The global precipitation sensor market size was valued at USD 820 million in 2023 and is projected to grow to USD 1,380 million by 2032, exhibiting a compound annual growth rate (CAGR) of 5.9% from 2024 to 2032. The market's growth is significantly driven by increasing climate variability and the resultant need for accurate weather monitoring systems.

One of the primary growth factors driving the precipitation sensor market is the growing awareness regarding climate change and its impacts. Governments and organizations worldwide are investing heavily in advanced weather monitoring systems to predict and mitigate the adverse effects of climate-related events. This increased emphasis on climate resilience is fostering the demand for sophisticated precipitation sensors. Furthermore, the technological advancements in sensor technology, particularly in terms of accuracy and reliability, are significantly contributing to market growth. Modern sensors equipped with advanced features offer precise measurements, which are critical for accurate weather forecasting and research.

Another key factor propelling the market is the rising adoption of precision agriculture practices. Farmers are increasingly relying on precipitation data to optimize irrigation, improve crop yield, and reduce waste. The integration of Internet of Things (IoT) in agriculture has further amplified the use of precipitation sensors, enabling real-time data collection and analysis. Additionally, urbanization and the consequent need for efficient water management systems in cities are also driving the demand for advanced precipitation monitoring solutions. Cities are implementing smart water management systems that heavily depend on accurate precipitation data to prevent flooding and manage water resources efficiently.

The transportation sector is also a significant contributor to the growing demand for precipitation sensors. Accurate weather data is crucial for ensuring safety and efficiency in transportation networks, including aviation, maritime, and road transport. Precipitation sensors are used to monitor weather conditions and provide real-time data to improve decision-making processes, thereby reducing risks and enhancing operational efficiency. Moreover, the increased focus on disaster management and the need to provide timely alerts during extreme weather conditions are further augmenting the market growth. Governments and disaster management agencies are deploying advanced precipitation sensors to enhance their preparedness and response strategies.

Regionally, North America and Europe are the leading markets for precipitation sensors, driven by the presence of advanced meteorological infrastructure and high awareness regarding climate change. In North America, the United States is a major contributor due to its significant investments in weather monitoring and disaster management systems. Europe, with its stringent environmental regulations and proactive climate policies, also presents substantial growth opportunities. Meanwhile, the Asia Pacific region is expected to exhibit the highest growth rate during the forecast period. The increasing frequency of extreme weather events, coupled with the growing adoption of smart agriculture practices, is driving the demand for precipitation sensors in countries like China, India, and Japan. Latin America and the Middle East & Africa regions are also witnessing steady growth, primarily driven by investments in improving their weather monitoring capabilities.

Product Type Analysis

The precipitation sensor market is segmented by product type into Rain Gauges, Disdrometers, Optical Rain Sensors, and Others. Rain gauges are the most traditional form of precipitation sensors and have been widely used for decades in various applications. They are simple to use, cost-effective, and provide reliable measurements. Despite the emergence of more advanced technologies, rain gauges continue to hold a significant market share, especially in areas where simplicity and cost-effectiveness are prioritized. The ongoing advancements in rain gauge technology, such as automated data logging and remote monitoring capabilities, are enhancing their appeal and utility.

Disdrometers represent a more sophisticated class of precipitation sensors, capable of providing detailed information about the size and velocity of raindrops. These sensors are crucial in advanced meteorological studies and research, offering detailed precipitation data that is essential for understanding complex weather patterns. The adoption of disdrometers is growing,

The U.S. Geological Survey (USGS) in cooperation with the City of Grandview, Missouri, assessed flooding of the Little Blue River at Grandview resulting from precipitation events of varying recurrence intervals and durations, and expected changes in land cover. The precipitation scenarios were used to develop a library of flood-inundation maps that included a 3.5-mile reach of the Little Blue River and tributaries within and adjacent to the city. A hydrologic model of the upper Little Blue River Basin, and hydraulic model of a selected study reach of the Little Blue River and tributaries were constructed to assess streamflow magnitudes associated with simulated precipitation amounts and the resulting flood-inundation conditions. The U.S. Army Corps of Engineers Hydrologic Engineering Center-Hydrologic Modeling System (HEC–HMS) version 4.41 was used to simulate the amount of streamflow produced from 54 precipitation events. The Hydrologic Engineering Center-River Analysis System (HEC–RAS) version 5.07 was then used to construct a steady-state hydraulic model to map resulting areas of flood inundation. The hydraulic and hydrologic models were calibrated to the May 28, 2020 high-flow event that produced a peak streamflow approximating a 10-percent annual exceedance probability (10-year flood-frequency recurrence interval) at the USGS streamgage Little Blue River at Grandview, Missouri (06893750). The calibrated HEC–HMS model was used to simulate streamflows from design precipitation events of 1- to 8-hour duration and ranging from a 100-percent to 0.2 percent annual exceedance probability. Flood-inundation maps were produced for USGS streamflow stages of 17 feet, or near bankfull, up to 23 feet, or stage exceeding the 0.2-percent annual exceedance interval flood using the HEC-RAS model. This data release includes: 1) a zip file (LittleBlueRiver_Grandview_HEC-HMS_model_archive.zip) containing all relevant files to document and run the Hydrological Engineering Center-Hydrologic Modeling System (HEC-HMS) one dimensional hydraulic model used to generate flood peaks for input in hydraulic model.

The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities.

Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office.

The following dataset from Fort Keogh (FTK) contains precipitation measurements in centimeter units and were aggregated to a yearly timescale.