Chesapeake Bay ROMS Community Model
Welcome to CCMP's ChesROMS homepage. This page will introduce you to the Chesapeake Bay ROMS Community Model (ChesROMS) as well as provide links to additional information and resources.
- Installation Walkthrough
- Project Summary
- ChesROMS on Sourceforge
ChesROMS is a community ocean modeling system for the Chesapeake Bay region being developed by scientists in NOAA, University of Maryland, CRC (Chesapeake Research Consortium) and MD DNR (Maryland Department of Natural Resources) supported by the NOAA MERHAB program. The model is built based on the Rutgers Regional Ocean Modeling System (ROMS, http://www.myroms.org/) with significant adaptations for the Chesapeake Bay.
The model is developed to provide a community modeling system for nowcast and forecast of 3D hydrodynamic circulation, temperature and salinity, sediment transport, biogeochemical and ecosystem states with applications to ecosystem and human health in the bay. Model validation is based on bay wide satellite remote sensing, real-time in situ measurements and historical data provided by Chesapeake Bay Program.
Forecasts based on ChesROMS from the Chesapeake Bay Forecasting System developed by ESSIC. ESSIC forecasts include Salinity, Water Temperature, Sea Nettles, Cholora Vibreo, SFC Currents and Algal Blooms.
ChesROMS forecasts are also avialble for Sea Nettles, Harmful Algal Blooms and Water Quality from the NOAA Chesaepake Bay Office (NCBO) website. The map below shows the current Sea Nettle forecast. Use the links to get to other NCBO forecasts.
Wen Long has created various video walkthroughs to guide you through the process of installing the ChesROMS model. These can be found at the links below:
The Installation of ChesROMS 1.1
by Wen Long
Note: If your media player does not properly play the movie, the VLC movie player is the recommended alternative. VLC Media player is available for free for Windows, Linux, and Mac at http://www.videolan.org/vlc/
Various noxious and toxic algal blooms afflict the Chesapeake Bay and other coastal U.S. waters, posing threats to human health and natural resources. The goal of this regional study is to develop and implement an operational system that will nowcast and forecast the likelihood of blooms of the following three harmful algal bloom (HAB) species in Chesapeake Bay and its tidal tributaries: the dinoflagellates Karlodinium micrum and Prorocentrum minimum and the cyanobacteria Microcystis aeruginosa. In addition, the feasibility of predicting other HAB species will be investigated and pursued. The method proposed involves using real-time and 3-day forecast data acquired and derived from a variety of sources and techniques to drive multi-variate empirical habitat models that predict the probability of blooms caused by these particular HAB species. The predictions, in the form of maps, will be available via the World Wide Web to individuals and interested agencies to guide research, recreational and management activities. In particular, these nowcasts and forecasts will be employed by the Maryland Department of Natural Resources (MD DNR) to guide their response sampling efforts for HAB monitoring. This approach builds directly upon the system that the PIs have implemented for nowcasting the likelihood of encountering sea nettles (Chrysaora quinquecirrha) and relative abundance of Karlodinium micrum in Chesapeake Bay and a new network of continuous in-situ monitors that have been deployed by MD DNR.
The operational HAB forecasting system will be constructed by 1) developing and implementing empirical habitat models for HAB species that predict the probability of a bloom as a function of each species preferred environmental conditions; 2) acquiring and forecasting the pertinent environmental variables in near-real time, using a combination of satellite remote sensing, real-time in situ measurement, and mechanistic 3-D modeling; 3) applying the habitat model of each species to the relevant environmental variables in order to nowcast and forecast the probability of their blooms throughout Chesapeake Bay and its tributaries; 4) validating the estimated environmental variables and verifying the HAB bloom predictions using in situ data collected by MD DNR and other data sources; and 5) enhancing an existing web page to disseminate these predictions of HAB bloom probability to managers, researchers, and the general public. The models, data, predictions and web site will be integrated into an operational forecasting system, built in accordance with NOAA / NOS forecast system standards, to routinely provide HAB predictions to the Maryland Department of Natural Resources -- the agency that is responsible for protecting living resources and human health in the bay -- and other users.
This multi-disciplinary project spans both the development and operationalization of HAB forecasting in Chesapeake Bay and will 1) provide an improved understanding of HABs and the factors that give rise to them, 2) develop and implement a methodology to predict the probable occurrence of blooms of important HAB species in Chesapeake Bay, and 3) implement a robust and automated HAB forecast system, created with and for the MD Department of Natural Resources, to provide early warnings of these extreme natural events and aid in mitigating the deleterious effects of their presence on human and ecosystem health in the bay.
- C. Brown (NOAA/NESDIS)
- R. Hood (UMCES-HPL)
- L. Lanerolle (NOAA/NOS/OCS)
- W. Long (UMCES-HPL)
- R. Murtugudde (ESSIC-UMD)
- B. Mathukumalli (ESSIC-UMD)
- J. Wiggert (USM-DMS)
- J. Xu (NOAA/NOS-UCAR)