Adding four-dimensional data assimilation by analysis nudging to the Model for Prediction Across Scales – Atmosphere (version 4.0)

The Model for Prediction Across Scales – Atmosphere (MPAS-A) has been modified to allow four-dimensional data assimilation (FDDA) by the nudging of temperature, humidity, and wind toward target values predefined on the MPAS-A computational mesh. The addition of nudging allows MPAS-A to be used as a global-scale meteorological driver for retrospective air quality modeling. The technique of analysis nudging developed for the Penn State/National Center for Atmospheric Research (NCAR) Mesoscale Model, and later applied in the Weather Research and Forecasting model, is implemented in MPAS-A with adaptations for its polygonal Voronoi mesh. Reference fields generated from 1°&thinsp; × &thinsp;1° National Centers for Environmental Prediction (NCEP) FNL (Final) Operational Global Analysis data were used to constrain MPAS-A simulations on a 92–25&thinsp;km variable-resolution mesh with refinement centered over the contiguous United States. Test simulations were conducted for January and July 2013 with and without FDDA, and compared to reference fields and near-surface meteorological observations. The results demonstrate that MPAS-A with analysis nudging has high fidelity to the reference data while still maintaining conservation of mass as in the unmodified model. The results also show that application of FDDA constrains model errors relative to 2&thinsp;m temperature, 2&thinsp;m water vapor mixing ratio, and 10&thinsp;m wind speed such that they continue to be at or below the magnitudes found at the start of each test period.</p

Implementation of an innovative, integrated electronic medical record (EMR) and public health information exchange for HIV/AIDS

Louisiana is severely affected by HIV/AIDS, ranking fifth in AIDS rates in the USA. The Louisiana Public Health Information Exchange (LaPHIE) is a novel, secure bi-directional public health information exchange, linking statewide public health surveillance data with electronic medical record data. LaPHIE alerts medical providers when individuals with HIV/AIDS who have not received HIV care for >12 months are seen at any ambulatory or inpatient facility in an integrated delivery network. Between 2/1/2009 and 1/31/2011, 488 alerts identified 345 HIV positive patients. Of those identified, 82% had at least one CD4 or HIV viral load test over the study follow-up period. LaPHIE is an innovative use of health information exchange based on surveillance data and real time clinical messaging, facilitating rapid provider notification of those in need of treatment. LaPHIE successfully reduces critical missed opportunities to intervene with individuals not in care, leveraging information historically collected solely for public health purposes, not health care delivery, to improve public health

Ten simple rules for working with high resolution remote sensing data

Researchers in Earth and environmental science can extract incredible value from high- resolution (sub-meter, sub-hourly or hyper-spectral) remote sensing data, but these data can be difficult to use. Correct, appropriate and competent use of such data requires skills from remote sensing and the data sciences that are rarely taught together. In practice, many researchers teach themselves how to use high-resolution remote sensing data with ad hoc trial and error processes, often resulting in wasted effort and resources. In order to implement a consistent strategy, we outline ten rules with examples from Earth and environmental science to help academic researchers and professionals in industry work more effectively and competently with high-resolution data

Assessing the feasibility of adaptation options: methodological advancements and directions for climate adaptation research and practice

The Paris Agreement put adaptation prominently on the global climate action agenda. Despite a surge in research and praxis-based knowledge on adaptation, a critical policy roadblock is synthesizing and assessing this burgeoning evidence. We develop an approach to assess the multidimensional feasibility of adaptation options in a robust and transparent manner, providing direction for global climate policy and identifying knowledge gaps to further future climate research. The approach, which was tested in the IPCC Special Report on 1.5 °C (SR1.5) to assess 23 adaptation options, is underpinned by a systematic review of recent literature, expert elicitation, and iterative peer review. It responds to the challenge of limited agreement on adaptation indicators, lack of fine-scale adaptation data, and challenges of assessing synergies and trade-offs with mitigation. The findings offer methodological insights into how future assessments such as the IPCC Assessment Report (AR) six and regional, national, and sectoral assessment exercises could assess adaptation feasibility and synthesize the growing body of knowledge on climate change adaptation