@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={Abstract The 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={Abstract The 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={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.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.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.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} }