skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.


Title: The Hawai‘i Climate Data Portal (HCDP)
Abstract The Hawai‘i Climate Data Portal (HCDP) is designed to facilitate streamlined access to a wide variety of climate data and information for the State of Hawai‘i. Prior to the development of the HCDP, gridded climate products and point datasets were fragmented, outdated, not easily accessible, and not available in near–real time. To address these limitations, HCDP researchers developed the cyberinfrastructure necessary to 1) operationalize data acquisition and product production in a near-real-time environment and 2) make data and products easily accessible to a wide range of users. The HCDP hosts several high-resolution (250 m) gridded products including monthly rainfall and daily temperature (maximum, minimum, and mean), station data, and gridded future projections of rainfall and temperature. HCDP users can visualize both gridded and point data, create and download custom maps, and query station and gridded data for export with relative ease. The “virtual station” feature allows users to create a climate time series at any grid point. The primary objective of the HCDP is to promote sharing and access to data and information to streamline research activities, improve awareness, and promote the development of tools and resources that can help to build adaptive capacities. The HCDP products have the potential to serve a wide range of users including researchers, resource managers, city planners, engineers, teachers, students, civil society organizations, and the broader community.  more » « less
Award ID(s):
2005506 2117975
PAR ID:
10546266
Author(s) / Creator(s):
; ; ; ; ; ; ; ; ; ;
Publisher / Repository:
American Meteorological Society
Date Published:
Journal Name:
Bulletin of the American Meteorological Society
Volume:
105
Issue:
7
ISSN:
0003-0007
Page Range / eLocation ID:
E1074 to E1083
Subject(s) / Keyword(s):
Rainfall Climate Temperature Downscaling Climate services
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. This paper discusses the design and implementation of the Hawai‘i Rainfall Analysis and Mapping Application (HI-RAMA) decision support tool. HI-RAMA provides researchers and community stakeholders interactive access to and visualization of hosted historical and near-real-time monthly rainfall maps and aggregated rainfall station observational data for the State of Hawai‘i. The University of Hawai‘i Information Technology Services Cyberinfrastructure team in partnership with members of the Hawai‘i Established Program to Stimulate Competitive Research (EPSCoR) ‘Ike Wai project team developed this application as part of the ‘Ike Wai Gateway to support water sustainability research for the state of Hawai‘i. This tool is designed to provide user-friendly access to information that can reveal the impacts of climate changes related to precipitation so users can make data-driven decisions. 
    more » « less
  2. Abstract The Hawaiian Islands have some of the most spatially diverse rainfall patterns on Earth, affected by prevailing trade winds, midlatitude disturbances, tropical cyclones, and complex island topography. However, it is the only state in the United States that does not have assigned climate divisions (boundaries defining climatically homogeneous areas), which excludes it from many national climate analyses. This study establishes, for the first time, official climate divisions for the state of Hawai‘i using cluster analysis applied to monthly gridded rainfall data from 1990 to 2019. Twelve climate divisions have been identified: two divisions were found each for the islands of Kaua‘i (Leeward Kaua‘i and Windward Kaua‘i), O‘ahu (Waianae and Ko‘olau), and Maui County (Leeward Maui Nui and Windward Maui Nui), and six divisions were identified for Hawai‘i Island (Leeward Kohala, Windward Kohala, Kona, Hawai‘i Mauka, Ka‘u, and Hilo). The climate divisions were validated using a statewide area-weighted division-average rainfall index which successfully captured the annual cycle and interannual rainfall variations in the statewide average rainfall series. Distinct rainfall seasonality features and interannual/decadal variability are found among the different divisions; Leeward Maui Nui, Leeward Kaua‘i, Kona, and Hawai‘i Mauka displayed the most significant rainfall seasonality. The western Hawai‘i Island divisions show the most significant long-term decreasing trends in annual rainfall during the past 100 years (ranging from −2.5% to −5.0% per decade). With these climate divisions now available, Hawai‘i will have access to numerous operational climate analyses that will greatly improve climatic research, monitoring, education, and outreach, as well as forecasting applications. Significance StatementThe Hawaiian Islands have some of the most spatially diverse climate patterns on Earth, but it is the only state in the United States that does not have assigned climate divisions, which excludes it from many national climate analyses. This paper establishes official climate divisions for the state of Hawai‘i, filling an incredibly important gap in the National Oceanic and Atmospheric Administration (NOAA)’s national coverage, moving toward better data equity and coverage outside the contiguous United States. Distinct rainfall seasonality features and interannual/decadal variability are revealed and compared among the different divisions. With these climate divisions now available, Hawai‘i will have access to numerous operational climate analyses that will greatly improve climatic research, monitoring, education, and outreach, as well as forecasting applications. 
    more » « less
  3. Abstract Gridded monthly rainfall estimates can be used for a number of research applications, including hydrologic modeling and weather forecasting. Automated interpolation algorithms, such as the “autoKrige” function in R, can produce gridded rainfall estimates that validate well but produce unrealistic spatial patterns. In this work, an optimized geostatistical kriging approach is used to interpolate relative rainfall anomalies, which are then combined with long-term means to develop the gridded estimates. The optimization consists of the following: 1) determining the most appropriate offset (constant) to use when log-transforming data; 2) eliminating poor quality data prior to interpolation; 3) detecting erroneous maps using a machine learning algorithm; and 4) selecting the most appropriate parameterization scheme for fitting the model used in the interpolation. Results of this effort include a 30-yr (1990–2019), high-resolution (250-m) gridded monthly rainfall time series for the state of Hawai‘i. Leave-one-out cross validation (LOOCV) is performed using an extensive network of 622 observation stations. LOOCV results are in good agreement with observations (R2= 0.78; MAE = 55 mm month−1; 1.4%); however, predictions can underestimate high rainfall observations (bias = 34 mm month−1; −1%) due to a well-known smoothing effect that occurs with kriging. This research highlights the fact that validation statistics should not be the sole source of error assessment and that default parameterizations for automated interpolation may need to be modified to produce realistic gridded rainfall surfaces. Data products can be accessed through the Hawai‘i Data Climate Portal (HCDP;http://www.hawaii.edu/climate-data-portal). Significance StatementA new method is developed to map rainfall in Hawai‘i using an optimized geostatistical kriging approach. A machine learning technique is used to detect erroneous rainfall maps and several conditions are implemented to select the optimal parameterization scheme for fitting the model used in the kriging interpolation. A key finding is that optimization of the interpolation approach is necessary because maps may validate well but have unrealistic spatial patterns. This approach demonstrates how, with a moderate amount of data, a low-level machine learning algorithm can be trained to evaluate and classify an unrealistic map output. 
    more » « less
  4. This dataset consists of the Surface Ocean CO2 Atlas Version 2022 (SOCATv2022) data product files. The ocean absorbs one quarter of the global CO2 emissions from human activity. The community-led Surface Ocean CO2 Atlas (www.socat.info) is key for the quantification of ocean CO2 uptake and its variation, now and in the future. SOCAT version 2022 has quality-controlled in situ surface ocean fCO2 (fugacity of CO2) measurements on ships, moorings, autonomous and drifting surface platforms for the global oceans and coastal seas from 1957 to 2021. The main synthesis and gridded products contain 33.7 million fCO2 values with an estimated accuracy of better than 5 μatm. A further 6.4 million fCO2 sensor data with an estimated accuracy of 5 to 10 μatm are separately available. During quality control, marine scientists assign a flag to each data set, as well as WOCE flags of 2 (good), 3 (questionable) or 4 (bad) to individual fCO2 values. Data sets are assigned flags of A and B for an estimated accuracy of better than 2 μatm, flags of C and D for an accuracy of better than 5 μatm and a flag of E for an accuracy of better than 10 μatm. Bakker et al. (2016) describe the quality control criteria used in SOCAT versions 3 to 2022. Quality control comments for individual data sets can be accessed via the SOCAT Data Set Viewer (www.socat.info). All data sets, where data quality has been deemed acceptable, have been made public. The main SOCAT synthesis files and the gridded products contain all data sets with an estimated accuracy of better than 5 µatm (data set flags of A to D) and fCO2 values with a WOCE flag of 2. Access to data sets with an estimated accuracy of 5 to 10 (flag of E) and fCO2 values with flags of 3 and 4 is via additional data products and the Data Set Viewer (Table 8 in Bakker et al., 2016). SOCAT publishes a global gridded product with a 1° longitude by 1° latitude resolution. A second product with a higher resolution of 0.25° longitude by 0.25° latitude is available for the coastal seas. The gridded products contain all data sets with an estimated accuracy of better than 5 µatm (data set flags of A to D) and fCO2 values with a WOCE flag of 2. Gridded products are available monthly, per year and per decade. Two powerful, interactive, online viewers, the Data Set Viewer and the Gridded Data Viewer (www.socat.info), enable investigation of the SOCAT synthesis and gridded data products. SOCAT data products can be downloaded. Matlab code is available for reading these files. Ocean Data View also provides access to the SOCAT data products (www.socat.info). SOCAT data products are discoverable, accessible and citable. The SOCAT Data Use Statement (www.socat.info) asks users to generously acknowledge the contribution of SOCAT scientists by invitation to co-authorship, especially for data providers in regional studies, and/or reference to relevant scientific articles. The SOCAT website (www.socat.info) provides a single access point for online viewers, downloadable data sets, the Data Use Statement, a list of contributors and an overview of scientific publications on and using SOCAT. Automation of data upload and initial data checks allows annual releases of SOCAT from version 4 onwards. SOCAT is used for quantification of ocean CO2 uptake and ocean acidification and for evaluation of climate models and sensor data. SOCAT products inform the annual Global Carbon Budget since 2013. The annual SOCAT releases by the SOCAT scientific community are a Voluntary Commitment for United Nations Sustainable Development Goal 14.3 (Reduce Ocean Acidification) (#OceanAction20464). More broadly the SOCAT releases contribute to UN SDG 13 (Climate Action) and SDG 14 (Life Below Water), and to the UN Decade of Ocean Science for Sustainable Development. Hundreds of peer-reviewed scientific publications and high-impact reports cite SOCAT. The SOCAT community-led synthesis product is a key step in the value chain based on in situ inorganic carbon measurements of the oceans, which provides policy makers with critical information on ocean CO2 uptake in climate negotiations. The need for accurate knowledge of global ocean CO2 uptake and its (future) variation makes sustained funding of in situ surface ocean CO2 observations imperative. 
    more » « less
  5. Abstract. As global atmosphere and ocean temperatures rise and the Greenland Ice Sheet loses mass, the glacial fjords of Kalaallit Nunaat/Greenland play an increasingly critical role in our climate system. Fjords are pathways for freshwater from ice melt to reach the ocean and for deep, warm, nutrient-rich ocean waters to reach marine–terminating glaciers, supporting abundant local ecosystems that Greenlanders rely upon. Research in Greenland fjords has become more interdisciplinary and more observations are being collected in fjords than in previous decades. However, there are few long-term (> 10 years) datasets available for single fjords. Additionally, observations in fjords are often spatially and temporally disjointed, utilize multiple observing tools, and datasets are rarely provided in formats that are easily used across disciplines or audiences. We address this issue by providing standardized, gridded summer season hydrographic sections for Sermilik Fjord in Southeast Greenland, from 2009–2023. Gridded data facilitate the analysis of coherent spatial patterns across the fjord domain, and are a more accessible and intuitive data product compared to discrete profiles. We combined ship-based conductivity, temperature, and depth (CTD) profiles with helicopter-deployed eXpendable CTD (XCTD) profiles from the ice mélange region to create objectively mapped (or optimally interpolated) along-fjord sections of conservative temperature and absolute salinity. From the gridded data, we derived a summer season climatological mean and root mean square deviation, summarizing typical fjord conditions and highlighting regions of variability. This information can be used by model and laboratory studies, biological and ecosystem studies in the fjord, and provides context for interpreting previous work. Additionally, this method can be applied to datasets from other fjords helping to facilitate fjord intercomparison studies. The gridded data and climatological products are available in netCDF format at https://doi.org/10.18739/A28G8FK6D (Roth et al., 2025a). All original profile observations, with unique DOIs for each field campaign, are available through the Sermilik Fjord Hydrography Data Portal (https://arcticdata.io/catalog/portals/sermilik, last access: 7 November 2025) hosted by the Arctic Data Center (Straneo et al., 2025). The code used has also been made available to facilitate continued updates to the Sermilik Fjord gridded section dataset and applications to other fjord systems. 
    more » « less