@article{huang_yin_carton_chen_graham_hogan_smith_zhang_2024, title={Record High Sea Surface Temperatures in 2023}, volume={51}, ISSN={["1944-8007"]}, DOI={10.1029/2024GL108369}, abstractNote={Abstract NOAA Daily Optimum Interpolation Sea Surface Temperature (DOISST) and other similar sea surface temperature (SST) products indicate that the globally averaged SST set a new daily record in March 2023. The record‐high SST in March was immediately broken in April, and new daily records were set again in July and August 2023. The SST anomaly (SSTA) persisted at a record high from mid‐March to the remainder of 2023. Our analysis indicates that the record‐high SSTs, and associated marine heatwaves (MHWs) and even super‐MHWs, are attributed to three factors: (a) a long‐term warming trend, (b) a shift to the warm phase of the multi‐decadal Pacific‐Atlantic‐Arctic (PAA) mode, and (c) the transition from the triple‐dip succession of La Niña events to the 2023–24 El Niño event.}, number={14}, journal={GEOPHYSICAL RESEARCH LETTERS}, author={Huang, Boyin and Yin, Xungang and Carton, James A. and Chen, Ligang and Graham, Garrett and Hogan, Patrick and Smith, Thomas and Zhang, Huai-Min}, year={2024}, month={Jul} }
 @article{yin_huang_carton_chen_graham_liu_smith_zhang_2023, title={The 1991-2020 sea surface temperature normals}, ISSN={["1097-0088"]}, DOI={10.1002/joc.8350}, abstractNote={AbstractThe 1991–2020 climate normals for sea surface temperature (SST) are computed based on the NOAA Daily Optimum Interpolation SST dataset. This is the first time that high‐resolution SST normals with global coverage can be achieved in the satellite SST era. Normals are one of the fundamental parameters in describing and understanding weather and climate and provide decision‐making information to industry, public, and scientific communities. This product suite includes SST mean, standard deviation, count and extreme parameters at daily, monthly, seasonal and annual time scales on 0.25° spatial grids. The main feature of the SST mean state revealed by the normals is that in the Tropics, the Indo‐Pacific Ocean is dominated by the warm pool (SST ≥ 28°C) while the eastern Pacific is characterized by the cold tongue (SST ≤ 24°C); in the midlatitudes, SSTs are in zonal patterns with high meridional gradients. Daily SST standard deviations are generally small (<1.0°C) except in frontal zones (>1.5°C) mostly associated with ocean currents such as the Gulf Stream, Kuroshio and Equatorial Currents. Compared to the 1982–2011 climatology, the 1991–2020 mean SSTs increased over most global areas but obvious cooling is seen in the Southern Ocean, eastern tropical South Pacific Ocean and North Atlantic warming hole. The Indo‐Pacific warm pool (IPWP) is found to have strengthened in both intensity and coverage since 1982–2011. By a count parameter criterion of ≥300 days annually with SST ≥ 28°C, the IPWP coverage increased 33% from 1982–2011 to 1991–2020. The global mean SST of 1991–2020 is warmer than that of 1982–2011, and the warming rate over 1991–2020 doubles that over 1901–2020.}, journal={INTERNATIONAL JOURNAL OF CLIMATOLOGY}, author={Yin, Xungang and Huang, Boyin and Carton, James A. and Chen, Ligang and Graham, Garrett and Liu, Chunying and Smith, Thomas and Zhang, Huai-Min}, year={2023}, month={Dec} }
 @article{huang_liu_freeman_graham_smith_zhang_2021, title={Assessment and Intercomparison of NOAA Daily Optimum Interpolation Sea Surface Temperature (DOISST) Version 2.1}, volume={34}, ISSN={["1520-0442"]}, DOI={10.1175/JCLI-D-21-0001.1}, abstractNote={AbstractThe NOAA Daily Optimum Interpolation Sea Surface Temperature dataset (DOISST) has recently been updated to v2.1 (January 2016–present). Its accuracy may impact the climate assessment, monitoring and prediction, and environment-related applications. Its performance, together with those of seven other well-known sea surface temperature (SST) products, is assessed by comparison with buoy and Argo observations in the global oceans on daily 0.25° × 0.25° resolution from January 2016 to June 2020. These seven SST products are NASA MUR25, GHRSST GMPE, BoM GAMSSA, UKMO OSTIA, NOAA GPB, ESA CCI, and CMC. Our assessments indicate that biases and root-mean-square difference (RMSDs) in reference to all buoys and all Argo floats are low in DOISST. The bias in reference to the independent 10% of buoy SSTs remains low in DOISST, but the RMSD is slightly higher in DOISST than in OSTIA and CMC. The biases in reference to the independent 10% of Argo observations are low in CMC, DOISST, and GMPE; also, RMSDs are low in GMPE and CMC. The biases are similar in GAMSSA, OSTIA, GPB, and CCI whether they are compared against all buoys, all Argo, or the 10% of buoy or 10% of Argo observations, while the RMSDs against Argo observations are slightly smaller than those against buoy observations. These features indicate a good performance of DOISST v2.1 among the eight products, which may benefit from ingesting the Argo observations by expanding global and regional spatial coverage of in situ observations for effective bias correction of satellite data.}, number={18}, journal={JOURNAL OF CLIMATE}, author={Huang, Boyin and Liu, Chunying and Freeman, Eric and Graham, Garrett and Smith, Tom and Zhang, Huai-Min}, year={2021}, month={Sep}, pages={7421–7441} }
 @article{huang_liu_banzon_freeman_graham_hankins_smith_zhang_2021, title={Improvements of the Daily Optimum Interpolation Sea Surface Temperature (DOISST) Version 2.1}, volume={34}, ISSN={["1520-0442"]}, DOI={10.1175/JCLI-D-20-0166.1}, abstractNote={AbstractThe NOAA/NESDIS/NCEI Daily Optimum Interpolation Sea Surface Temperature (SST), version 2.0, dataset (DOISST v2.0) is a blend of in situ ship and buoy SSTs with satellite SSTs derived from the Advanced Very High Resolution Radiometer (AVHRR). DOISST v2.0 exhibited a cold bias in the Indian, South Pacific, and South Atlantic Oceans that is due to a lack of ingested drifting-buoy SSTs in the system, which resulted from a gradual data format change from the traditional alphanumeric codes (TAC) to the binary universal form for the representation of meteorological data (BUFR). The cold bias against Argo was about −0.14°C on global average and −0.28°C in the Indian Ocean from January 2016 to August 2019. We explored the reasons for these cold biases through six progressive experiments. These experiments showed that the cold biases can be effectively reduced by adjusting ship SSTs with available buoy SSTs, using the latest available ICOADS R3.0.2 derived from merging BUFR and TAC, as well as by including Argo observations above 5-m depth. The impact of using the satellite MetOp-B instead of NOAA-19 was notable for high-latitude oceans but small on global average, since their biases are adjusted using in situ SSTs. In addition, the warm SSTs in the Arctic were improved by applying a freezing point instead of regressed ice-SST proxy. This paper describes an upgraded version, DOISST v2.1, which addresses biases in v2.0. Overall, by updating v2.0 to v2.1, the biases are reduced to −0.07° and −0.14°C in the global ocean and Indian Ocean, respectively, when compared with independent Argo observations and are reduced to −0.04° and −0.08°C in the global ocean and Indian Ocean, respectively, when compared with dependent Argo observations. The difference against the Group for High Resolution SST (GHRSST) Multiproduct Ensemble (GMPE) product is reduced from −0.09° to −0.01°C in the global oceans and from −0.20° to −0.04°C in the Indian Ocean.}, number={8}, journal={JOURNAL OF CLIMATE}, author={Huang, Boyin and Liu, Chunying and Banzon, Viva and Freeman, Eric and Graham, Garrett and Hankins, Bill and Smith, Tom and Zhang, Huai-Min}, year={2021}, month={Apr}, pages={2923–2939} }
 @article{runkle_sugg_graham_hodge_march_mullendore_tove_salyers_valeika_vaughan_2021, title={Participatory COVID-19 Surveillance Tool in Rural Appalachia Real-Time Disease Monitoring and Regional Response}, volume={136}, ISSN={["1468-2877"]}, DOI={10.1177/0033354921990372}, abstractNote={Introduction Few US studies have examined the usefulness of participatory surveillance during the coronavirus disease 2019 (COVID-19) pandemic for enhancing local health response efforts, particularly in rural settings. We report on the development and implementation of an internet-based COVID-19 participatory surveillance tool in rural Appalachia. Methods A regional collaboration among public health partners culminated in the design and implementation of the COVID-19 Self-Checker, a local online symptom tracker. The tool collected data on participant demographic characteristics and health history. County residents were then invited to take part in an automated daily electronic follow-up to monitor symptom progression, assess barriers to care and testing, and collect data on COVID-19 test results and symptom resolution. Results Nearly 6500 county residents visited and 1755 residents completed the COVID-19 Self-Checker from April 30 through June 9, 2020. Of the 579 residents who reported severe or mild COVID-19 symptoms, COVID-19 symptoms were primarily reported among women (n = 408, 70.5%), adults with preexisting health conditions (n = 246, 70.5%), adults aged 18-44 (n = 301, 52.0%), and users who reported not having a health care provider (n = 131, 22.6%). Initial findings showed underrepresentation of some racial/ethnic and non–English-speaking groups. Practical Implications This low-cost internet-based platform provided a flexible means to collect participatory surveillance data on local changes in COVID-19 symptoms and adapt to guidance. Data from this tool can be used to monitor the efficacy of public health response measures at the local level in rural Appalachia. }, number={3}, journal={PUBLIC HEALTH REPORTS}, author={Runkle, Jennifer D. and Sugg, Maggie M. and Graham, Garrett and Hodge, Bryan and March, Terri and Mullendore, Jennifer and Tove, Fletcher and Salyers, Martha and Valeika, Steve and Vaughan, Ellis}, year={2021}, month={May}, pages={327–337} }