Cooperative Observer Research Articles

Have climate change and warmer winters altered freeze-thaw patterns?

Although an increase in the number of freeze–thaw (F/T) events are known to cause increased damage to pavement systems, further research is required to understand the impact of climate change on F/T cycles. This study aimed to determine the frequency of F/T cycles, and hence damage events, at typical air temperature sensor levels, in various layers of pavement, and at critical subgrade locations. In response to global warming, the study considered rigorously computed F/T events rooted in Minnesota climate data between 1901 and 2020. Cooperative Observer Network (COOP) and Modern-Era Retrospective Analysis Version 2.0 (MERRA-2) data were employed to examine subsurface warming conditions. The number of F/T cycles at the air temperature sensor level and individual pavement layers was determined by utilizing historical data from the COOP weather stations and Minnesota Road Test Facility (MnROAD). The evaluation was conducted on both a monthly and yearly basis. Results indicated that starting from 1981, Minnesota winters have become warmer by 0.1 to 1.2 °C in the daytime and by 0.3 to 1.5 °C during nighttime. The warmer winter weather results in fewer F/T cycles at shallow pavement levels on an annual basis while remaining erratic at deeper sub-pavement locations. The reduction of F/T events at shallower pavement depths was substantial during the late fall and early spring months. While monthly composites of F/T events at air-temperature sensor levels also reflected a decrease in F/T cycles during late fall and spring, the annual frequency of F/T events was randomly distributed.

기후변화 적응분야의 시민과학 활용 동향과 시사점

Citizen science is a promising strategy for establishment of climate change adaptation policy in that it can effectively integrate community-based adaptation needs into policy. This study aims to suggest key consideration points by analyzing the world-wide representative citizen science projects so that citizen science can be well utilized when setting up adaptation policies. To select the representative projects, we searched Web of Science for papers including keywords ‘climate change adaptation,’ ‘citizen engagement,’ or ‘public participation’ in the title or abstract. We classified the papers by main topics of ‘climate monitoring,’ ‘stakeholder participation,’ ‘governance establishment,’ and ‘education-based awareness-raising.’ Four representative projects were chosen for each topic. The project ‘Cooperative Observer Program’ by the National Weather Service was chosen for ‘climate monitoring’ because it provided the longest-term data to predict climate change. The project ‘ClimMob’ by the Consultative Group on International Agricultural Research was selected for ‘stakeholder participation’ because of the active involvement of stakeholders. The project ‘Transforming Climate Knowledge with and for Society’ by the Research Council of Norway was an example of ‘governance establishment’ as the local community participated in the entire process from climate monitoring to policy evaluation. The project ‘Sandwatch’ by UNESCO was chosen in ‘education-based awareness-raising’ since the encouraged awareness had led the participating children to adapt spontaneously to climate change as adults. We conducted SWOT (strengths, weaknesses, opportunities, threats) analysis of each project and drew implications based on the results. For citizen science to be applied properly to climate change adaptation policy setup, the project should involve stakeholders in all processes and include proper incentive for their contribution. Furthermore, to avoid misleading policy directions and wasting resources, it is necessary to operate selective citizen science projects appropriate for accomplishing the policy goal.

Distributed observers design for a class of nonlinear systems to achieve omniscience asymptotically via differential geometry

AbstractDistributed observers are the framework of fusing observed data by constructing a cooperative observer network. This article renders a scheme of distributed observers for a class of nonlinear system. With this scheme, each local nonlinear observer in the cooperative network can estimate the whole states of the underlying system by their own output measurements and information obtained from their neighbor via communication network. The proposed distributed observers are constructed with Partial Observable Canonical Form, which is a technology studied in differential geometry. The common observable subspace pruning and the weight matrix with spatial transformation are innovatively developed to solve the problem that each local observer is not in the same space. The sufficient conditions are proposed to guarantee the achievement of asymptotical omniscience of distributed nonlinear observers. Furthermore, our distributed observers can also be applied to linear systems and systems with some zero outputs. In addition, comparing with some existing methods of distributed nonlinear observers, our method admits some unbounded systems. Four simulations including multi‐Wen‐bridge oscillator systems, multi‐manipulators systems, unbounded nonlinear system and a linear system are employed to show the validity of the main result.

Evaluation and blending of ATMS and AMSR2 snow water equivalent retrievals over the conterminous United States

This study first compares two different passive microwave snow water equivalent (SWE) retrievals, namely the retrieval from the Suomi National Polar-orbiting Partnership (S-NPP) Advanced Technology Microwave Sounder (ATMS) and that from the Global Change Observation Mission – Water (GCOM-W1) Advanced Microwave Scanning Radiometer 2 (AMSR2); it further creates an optimal blending mechanism that merges the two retrievals with in situ observations from the Snow Telemetry (SNOTEL) and Cooperative Observer Program (COOP) networks. The assessments of the two products are done over conterminous United States (CONUS) for the snow seasons (November–June) of the water years 2017–2019 using in situ data and the SNOw Data Assimilation System (SNODAS) SWE analysis. Both satellite products tend to underestimate SWE. Between the two, AMSR2 retrieval outperforms in terms of correlation with observations and depth of saturation, but it exhibits a distinctive, seasonally varying bias that is not seen in ATMS retrieval. The negative bias over the early snow season, as further analysis indicates, most likely stems from AMSR2 retrieval's use of a high frequency channel (i.e., 89 GHz) for shallow snow detection, while the impact of differing assumptions of snow density is marginal. The blending scheme, developed on the basis of the validation experiment, features a histogram-based bias correction as a supplement to optimal interpolation. Cross-validation suggests that interpolated station product without the satellite background broadly underperforms the blended in situ-satellite product, confirming the utility of the satellite retrievals. Furthermore, the a priori bias correction mechanism is shown to be effective in mitigating large fluctuations in bias. Finally, the bias-corrected, blended in situ-satellite product performs comparably or even favorably against SNODAS over many parts of the CONUS, with important implications for joint use of satellite and in situ observations for hydrological monitoring and forecasting.

Quality Control and Processing of Cooperative Observer Program Hourly Precipitation Data

AbstractThe National Oceanic and Atmospheric Administration (NOAA) has operated a network of Fischer & Porter gauges providing hourly and subhourly precipitation observations as part of the U.S. Cooperative Observer Program since the middle of the twentieth century. A transition from punched paper recording to digital recording was completed by NOAA’s National Weather Service in 2013. Subsequently, NOAA’s National Centers for Environmental Information (NCEI) upgraded its quality assurance and data stewardship processes to accommodate the new digital record, better assure the quality of the data, and improve the timeliness by which hourly precipitation observations are made available to the user community. Automated methods for removing noise, detecting diurnal variations, and identifying malfunctioning gauges are described along with quality control algorithms that are applied on hourly and daily time scales. The quality of the hourly observations during the digital era is verified by comparison with hourly observations from the U.S. Climate Reference Network and summary of the day precipitation totals from the Global Historical Climatology Network dataset.

A comparison of extreme precipitation event frequency and magnitude using a high‐resolution rain gage network and NOAA Atlas 14 across Delaware

AbstractExtreme precipitation events are arguably one of the most important natural hazards in many areas of the globe, impacting nearly every societal sector. In the Northeastern United States, extreme precipitation events have been shown to be increasing with several recent events garnering national attention (i.e., Ellicott City Maryland 2018; Tropical Storm Lee 2011). The NOAA Atlas 14 product is the nation's standard for estimating the magnitude and frequency of site‐specific extreme precipitation events, containing both precipitation frequency estimates, as well as associated confidence intervals. The Atlas uses surface stations, primarily from the National Weather Service Cooperative Observer Program, and statistical methodologies to provide point‐based precipitation exceedance probability estimates for several durations and potential recurrence intervals. Unfortunately, the number and quality of Cooperative Observer sites varies greatly over space and time. This research compares observed precipitation extremes from a high‐resolution statewide mesonet to those estimated by the Atlas 14 product for a 10‐year recurrence interval at several precipitation durations. Results of the analysis indicate that Atlas 14 underestimates the number and magnitude of extreme precipitation events across the state of Delaware at longer event durations (360‐ to 1,440‐min). At shorter durations (5‐ to 240‐min) the Atlas 14 estimates are more closely aligned with the observations from the high‐resolution precipitation network. These results suggest that caution should be exercised when using Atlas 14 estimates for engineering standards and hydrologic studies, especially for longer duration events. Therefore, a more rapid update cycle for revision of the Atlas 14 product should be considered, as a changing climate regime may be responsible for the differences identified in this research.

Who Received the Most Rain Today?: An Analysis of Daily Precipitation Extremes in the Contiguous United States Using CoCoRaHS and COOP Reports

AbstractEvery day, thousands of volunteers across the United States report the amount of precipitation they have received in the past 24 hours. This study focuses on the largest of these volunteer-submitted reports for each day, using precipitation measurements from the Community Collaborative Rain, Hail and Snow Network (CoCoRaHS) from January 2010 to December 2017 as well as observations from the U.S. Cooperative Observer Program (COOP) network from January 1981 through December 2017. Results provide clarity on spatial variability, temporal variability, and seasonal cycle of contiguous U.S daily precipitation extremes (DPEs). During 2010–17, the DPEs ranged from 11 mm on 28 March 2013 in Oregon to 635 mm on 27 August 2017 in Texas during Hurricane Harvey. Coastal states are most prone to high daily precipitation totals, especially those bordering the Gulf of Mexico or Atlantic Gulf Stream. The average DPE value varies with season; it is greater than 175 mm in late August and less than 100 mm through meteorological winter. These observations also show that location of the DPE varies with season as well. For example, 28.5% of February extremes fall in Pacific states, whereas all August extremes occur east of that region. Perhaps most importantly, these findings demonstrate strength in numbers. The large daily sample size of CoCoRaHS and COOP networks forms a basis for monitoring, mapping, and categorizing DPEs, and other aspects of extreme precipitation, with considerable spatial detail.

Evaluating the Reliability of the U.S. Cooperative Observer Program Precipitation Observations for Extreme Events Analysis Using the LTAR Network

AbstractThe detection and attribution of changes in precipitation characteristics relies on dense networks of rain gauges. In the United States, the COOP network is widely used for such studies even though there are reported inconsistencies due to changes in instruments and location, inadequate maintenance, dissimilar observation time, and the fact that measurements are made by a group of dedicated volunteers. Alternately, the Long-Term Agroecosystem Research (LTAR) network has been consistently and professionally measuring precipitation since the early 1930s. The purpose of this study is to compare changes in extreme daily precipitation characteristics during the warm season using paired rain gauges from the LTAR and COOP networks. The comparison, done at 12 LTAR sites located across the United States, shows underestimation and overestimation of daily precipitation totals at the COOP sites compared to the reference LTAR observations. However, the magnitude and direction of the differences are not linked to the underlying precipitation climatology of the sites. Precipitation indices that focus on extreme precipitation characteristics match closely between the two networks at most of the sites. Our results show consistency between the COOP and LTAR networks with precipitation extremes. It also indicates that despite the discrepancies at the daily time steps, the extreme precipitation observed by COOP rain gauges can be reliably used to characterize changes in the hydrologic cycle due to natural and human causes.

On the frequency of lake-effect snowfall in the Catskill Mountains

ABSTRACTMeltwater from snow that falls in the Catskill/Delaware watershed in the Catskill Mountains in south-central New York contributes to reservoirs that supply drinking water to approximately nine million people in and near New York City (NYC). Using the Interactive Multisensor Snow and Ice Mapping System (IMS) 4-km snow maps from the National Oceanic and Atmospheric Administration’s National Ice Center, we identified and tracked 28 lake-effect (LE) storms that deposited snow in the Catskills (2004–2017). These storms, which generally originated from Lake Ontario, but sometimes from Lake Erie, represent an underestimate of the number of LE storms that contribute snowfall to the Catskills snowpack because snowstorms are not visible on IMS maps when they travel over already-snow-covered terrain. Using satellite, meteorological (including NEXRAD and National Weather Service Cooperative Observer Program), and reanalysis data, we identified conditions that contributed to the LE snowstorms and mapped snow-cover extent (SCE) following the storms. We show that LE snow makes an important contribution to the Catskills snowpack because of the frequency of events, even though the total amount of each event may, on average, be small. The IMS 4-km maps tend to overestimate SCE compared to MODerate-resolution Imaging Spectroradiometer and Landsat snow maps. Climate change could impact LE storms and the distribution of rain versus snow in the Catskills, which may affect future reservoir operations in the NYC Water Supply System and winter recreation in the Catskills.

Observing the May 2015 Record Rainfall at Norman, Oklahoma, Using Various Methods

Abstract The May 2015 record rainfall that occurred across Oklahoma was the result of a large number of high-intensity rain events. A unique set of observations from gauges in the Oklahoma Mesonet, the NWS Cooperative Observer (COOP) network, the Community Collaborative Rain, Hail and Snow (CoCoRaHS) network, an experimental pit gauge system, and NWS radar was available that covered an area in and around Norman, Oklahoma. This paper documents the performance of the various gauges throughout the course of the month. Key findings are 1) observations from all methods significantly exceeded the 200-yr return interval; 2) a weighing-bucket gauge at ground level recorded amounts up to 4.5% higher than a similarly located ground-level tipping-bucket gauge and up to 8.2% higher than a nearby aboveground tipping-bucket gauge; 3) a manual COOP gauge recorded nearly identical (within 1.2%) observations as compared to an automated tipping-bucket gauge at a collocated Mesonet station; and 4) observations from 26 CoCoRaHS gauges yielded an average rainfall within 1% of the aerially averaged radar rainfall derived from the Multisensor Precipitation Estimator.

A Multialgorithm Reanalysis-Based Freezing-Precipitation Dataset for Climate Studies in the South-Central United States

AbstractFreezing precipitation, including freezing rain, freezing drizzle, and ice pellets, presents substantial hazards to transportation, energy, and infrastructure. Most quantitative climate-length (>30 years) datasets for freezing precipitation are obtained from in situ instrumentation, such as National Weather Service (NWS) automated observing systems or the NWS Cooperative Observer Program, that can be spatially and temporally inhomogeneous. This work investigates whether the 32-km North American Regional Reanalysis (NARR) is a viable dataset for developing a comprehensive and high-resolution spatially gridded dataset for freezing precipitation and its associated meteorological environment, with a focus on the south-central United States. NARR includes categorical precipitation type as a variable; to permit a translatable method across other gridded multidimensional reanalyses, however, a multialgorithm approach is also used to extract environmental conditions, event counts, and liquid water equivalent (LWE) for freezing precipitation, defined as total accumulated freezing rain and ice pellets. The resulting datasets are evaluated spatially and temporally against hourly and daily event counts and LWE compiled from 13 first-order NWS stations, the National Centers for Environmental Information Storm Data product, and “meteorological phenomena identification near the ground” (mPING) observations. Very good statistical agreement is evident for many of the station sites, and NARR is able, in most cases, to reproduce years with heavy-ice events and the spatial extent of such events. Climatological freezing precipitation tends to be underestimated in the western subdomain and overestimated in the south. It is concluded that the derived datasets could be a useful tool for climatological research and hazard analysis, with some caveats.

Understanding the Influence of Tropical Cyclone Landfall Central Pressure and Accumulated Rainfall on Storm Surge Near New Orleans, Louisiana

ABSTRACT Trepanier, J.C.; Needham, H.F., and Ellis, K.N., 2018. Understanding the influence of tropical cyclone landfall central pressure and accumulated rainfall on storm surge near New Orleans, Louisiana. Tropical cyclone track, landfall central pressure, accumulated cyclone rain, and storm surge are assessed for five stations surrounding Lake Pontchartrain in Louisiana: Shell Beach, the New Orleans Lakefront, Frenier, Mandeville, and Slidell. Floods from 1901–2012 are analyzed for cases in which the event tracks, the landfall central pressure, the amount of inland flooding from precipitation, and the storm-surge inundation all lead to compound effects, as in Hurricane Katrina (2005) and Hurricane Isaac (2012). Data are from U-Surge, the National Hurricane Center, and the National Weather Service Cooperative Observer Program. A storm track climatology is provided showing the average track for all events and for events that reached at least 43 m s−1. The intense storms have a more concentrated track and ...

A Comparison of Temperature Data from Automated and Manual Observing Networks in Georgia and Impacts of Siting Characteristics

AbstractThe objectives of this study were to compare average monthly and seasonal maximum and minimum temperatures of the Georgia Automated Environmental Monitoring Network (AEMN) to those of geographically close (i.e., paired) manual observations from U.S. Historical Climatology Network (USHCN) stations and Cooperative Observer Program (COOP) stations for the period 2002–13, and to evaluate the extent to which differences in siting characteristics of paired AEMN–USHCN stations contribute to the temperature differences. Correlations for monthly and seasonal maximum and minimum temperatures of paired AEMN–USHCN and AEMN–COOP stations were high and almost always significant, although the correlations for seasonal minimum temperatures were slightly lower than those of maximum temperatures, especially for summer. Monthly maximum and minimum temperatures and seasonal maximum temperatures of paired AEMN–USHCN and AEMN–COOP stations were significantly different in only a few instances, while seasonal minimum temperatures were more often significantly different, particularly for summer. The stronger relationship between maximum temperatures than minimum temperatures for paired stations is logical given that minimum temperatures typically occur when a shallow, decoupled nocturnal boundary layer is more sensitive to local conditions [e.g., land use/land cover (LULC)]. Stepwise regressions confirmed that a portion of the variance of seasonal minimum temperatures of paired AEMN–USHCN stations was explained by differences in LULC, while the variance in seasonal maximum temperatures was explained better by differences in elevation. Despite the generally close relationships between temperatures of paired stations and a portion of the differences being explained, an abrupt change from manual networks to the AEMN without data adjustments would change the Georgia climate record on monthly and seasonal time scales.

Spatial and temporal variations in the accuracy of meteorological drought indices

Meteorological drought indices are commonly calculated using data from weather stations and then interpolated to create a map of moisture conditions. These maps are used to communicate drought information to decision makers and the general public. This study analyzes five of the factors (drought index, interpolation method, seasonality, climate region, and station density) that influence the accuracy of these maps. This study compared the Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) using data from the Cooperative Observer Network (COOP) and United States Historical Climatology Network (USHCN). The accuracy of the drought maps varied significantly over time and space. The most significant factor affecting the accuracy of the meteorological drought maps was seasonality. Errors were higher in regions (e.g., southeastern U.S.), and months (e.g., summer), dominated by convective precipitation. The choice of interpolation method also had an influence. We found that Ordinary Kriging (OK) performed better than Inverse Distance Weighting (IDW) in all cases and therefore it was recommended for interpolating drought indices. Not surprisingly, maps that were created using more stations (COOP) were more accurate. The normalized errors of SPI and SPEI were very similar and so the choice of drought index had little impact on the accuracy of the drought maps.

Creation and Validation of a Comprehensive 1° by 1° Daily Gridded North American Dataset for 1900–2009: Snowfall

AbstractThis study details the creation of a gridded snowfall dataset for North America, with focus on the quality of the interpolated product. Daily snowfall amounts from National Weather Service Cooperative Observer Program stations and Meteorological Service of Canada surface stations are interpolated to 1° by 1° grids from 1900 to 2009 and examined for data record length and quality. The interpolation is validated spatially and temporally through the use of stratified sampling and k-fold cross-validation analyses. Interpolation errors average around 0.5 cm and range from less than 0.01 to greater than 2.5 cm. For most locations, this is within the measurement sensitivity. Grid cells with large variations in elevation experience higher errors and should be used with caution. A new gridded snowfall climatology is presented based on in situ observations that capture seasonal and interannual variability in monthly snowfall over most of the North American land area from 1949 to 2009. The Community Collaborative Rain, Hail and Snow Network is used as an independent set of point data that is compared to the gridded product. Errors are mainly in the form of the gridded data underestimating snowfall compared to the point data. The spatial extent, temporal length, and resolution of the dataset are unprecedented with regard to observational snowfall products and will present new opportunities for examining snowfall across North America.

High-Elevation Precipitation Patterns: Using Snow Measurements to Assess Daily Gridded Datasets across the Sierra Nevada, California*

AbstractGridded spatiotemporal maps of precipitation are essential for hydrometeorological and ecological analyses. In the United States, most of these datasets are developed using the Cooperative Observer (COOP) network of ground-based precipitation measurements, interpolation, and the Parameter–Elevation Regressions on Independent Slopes Model (PRISM) to map these measurements to places where data are not available. Here, we evaluate two daily datasets gridded at ° resolution against independent daily observations from over 100 snow pillows in California’s Sierra Nevada from 1990 to 2010. Over the entire period, the gridded datasets performed reasonably well, with median total water-year errors generally falling within ±10%. However, errors in individual storm events sometimes exceeded 50% for the median difference across all stations, and in many cases, the same underpredicted storms appear in both datasets. Synoptic analysis reveals that these underpredicted storms coincide with 700-hPa winds from the west or northwest, which are associated with post-cold-frontal flow and disproportionately small precipitation rates in low-elevation valley locations, where the COOP stations are primarily located. This atmospheric circulation leads to a stronger than normal valley-to-mountain precipitation gradient and underestimation of actual mountain precipitation. Because of the small average number of storms (<10) reaching California each year, these individual storm misses can lead to large biases (~20%) in total water-year precipitation and thereby significantly affect estimates of statewide water resources.

Observational Perspectives from U.S. Climate Reference Network (USCRN) and Cooperative Observer Program (COOP) Network: Temperature and Precipitation Comparison

AbstractThe U.S. Cooperative Observer Program (COOP) network was formed in the early 1890s to provide daily observations of temperature and precipitation. However, manual observations from naturally aspirated temperature sensors and unshielded precipitation gauges often led to uncertainties in atmospheric measurements. Advancements in observational technology (ventilated temperature sensors, well-shielded precipitation gauges) and measurement techniques (automation and redundant sensors), which improve observation quality, were adopted by NOAA’s National Climatic Data Center (NCDC) into the establishment of the U.S. Climate Reference Network (USCRN). USCRN was designed to provide high-quality and continuous observations to monitor long-term temperature and precipitation trends, and to provide an independent reference to compare to other networks. The purpose of this study is to evaluate how diverse technological and operational choices between the USCRN and COOP programs impact temperature and precipitation observations. Naturally aspirated COOP sensors generally had warmer (+0.48°C) daily maximum and cooler (−0.36°C) minimum temperatures than USCRN, with considerable variability among stations. For precipitation, COOP reported slightly more precipitation overall (1.5%) with network differences varying seasonally. COOP gauges were sensitive to wind biases (no shielding), which are enhanced over winter when COOP observed (10.7%) less precipitation than USCRN. Conversely, wetting factor and gauge evaporation, which dominate in summer, were sources of bias for USCRN, leading to wetter COOP observations over warmer months. Inconsistencies in COOP observations (e.g., multiday observations, time shifts, recording errors) complicated network comparisons and led to unique bias profiles that evolved over time with changes in instrumentation and primary observer.

Comparison of NCEP Multisensor Precipitation Estimates with Independent Gauge Data over the Eastern United States

AbstractGauge-calibrated radar estimates of daily precipitation are compared with daily observed values of precipitation from National Weather Service (NWS) Cooperative Observer Network (COOP) stations to evaluate the multisensor precipitation estimate (MPE) product that is gridded by the National Centers for Environmental Prediction (NCEP) for the eastern United States (defined as locations east of the Mississippi River). This study focuses on a broad evaluation of MPE across the study domain by season and intensity. In addition, the aspect of precipitation type is considered through case studies of winter and summer precipitation events across the domain. Results of this study indicate a north–south gradient in the error of MPE and a seasonal pattern with the highest error in summer and autumn and the lowest error in winter. Two case studies of precipitation are also considered in this study. These case studies include instances of intense precipitation and frozen precipitation. These results suggest that MPE is less able to estimate convective-scale precipitation as compared with precipitation variations at larger spatial scales. In addition, the results suggest that MPE is subject to errors related both to the measurement gauges and to the radar estimates used. Two case studies are also included to discuss the differences with regard to precipitation type. The results from these case studies suggest that MPE may have higher error associated with estimating the liquid equivalent of frozen precipitation when compared with NWS COOP network data. The results also suggest the need for more analysis of MPE error for frozen precipitation in diverse topographic regimes.

Spatiotemporal Snowfall Trends in Central New York

AbstractCentral New York State, located at the intersection of the northeastern United States and the Great Lakes basin, is impacted by snowfall produced by lake-effect and non-lake-effect snowstorms. The purpose of this study is to determine the spatiotemporal patterns of snowfall in central New York and their possible underlying causes. Ninety-three Cooperative Observer Program stations are used in this study. Spatiotemporal patterns are analyzed using simple linear regressions, Pearson correlations, principal component analysis to identify regional clustering, and spatial snowfall distribution maps in the ArcGIS software. There are three key findings. First, when the long-term snowfall trend (1931/32–2011/12) is divided into two halves, a strong increase is present during the first half (1931/32–1971/72), followed by a lesser decrease in the second half (1971/72–2011/12). This result suggests that snowfall trends behave nonlinearly over the period of record. Second, central New York spatial snowfall patterns are similar to those for the whole Great Lakes basin. For example, for five distinct regions identified within central New York, regions closer to and leeward of Lake Ontario experience higher snowfall trends than regions farther away and not leeward of the lake. Third, as compared with precipitation totals (0.02), average air temperatures had the largest significant (ρ < 0.05) correlation (−0.56) with seasonal snowfall totals in central New York. Findings from this study are valuable because they provide a basis for understanding snowfall patterns in a region that is affected by both non-lake-effect and lake-effect snowstorms.

How robust is the pre-1931 National Climatic Data Center—climate divisional dataset? Examples from Georgia and Louisiana

The National Climatic Data Center’s climate divisional dataset (CDD) is commonly used in climate change analyses. This dataset is a spatially continuous dataset for the conterminous USA from 1895 to the present. The CDD since 1931 is computed by averaging all available representative cooperative weather station data into a single monthly value for each of the 344 climate divisions of the conterminous USA, while pre-1931 data for climate divisions are derived from statewide averages using regression equations. This study examines the veracity of these pre-1931 data. All available Cooperative Observer Program (COOP) stations within each climate division in Georgia and Louisiana were averaged into a single monthly value for each month and each climate division from 1897 to 1930 to generate a divisional dataset (COOP DD), using similar methods to those used by the National Climatic Data Center to generate the post-1931 CDD. The reliability of the official CDD—derived from statewide averages—to produce temperature and precipitation means and trends prior to 1931 are then evaluated by comparing that dataset with the COOP DD with difference-of-means tests, correlations, and linear regression techniques. The CDD and the COOP DD are also compared to a divisional dataset derived from the United States Historical Climatology Network (USHCN) data (USHCN DD), with difference of means and correlation techniques, to demonstrate potential impacts of inhomogeneities within the CDD and the COOP DD. The statistical results, taken as a whole, not only indicate broad similarities between the CDD and COOP DD but also show that the CDD does not adequately portray pre-1931 temperature and precipitation in certain climate divisions within Georgia and Louisiana. In comparison with the USHCN DD, both the CDD and the COOP DD appear to be subject to biases that probably result from changing stations within climate divisions. As such, the CDD should be used judiciously for long-term studies of climate change, and past studies using the CDD should be evaluated in the context of these new findings.