Geoff Manikin

Bio: Geoff Manikin is an academic researcher from National Oceanic and Atmospheric Administration. The author has contributed to research in topics: Weather Research and Forecasting Model & Numerical weather prediction. The author has an hindex of 2, co-authored 3 publications receiving 2837 citations.

North american regional reanalysis

Abstract: In 1997, during the late stages of production of NCEP–NCAR Global Reanalysis (GR), exploration of a regional reanalysis project was suggested by the GR project's Advisory Committee, “particularly if the RDAS [Regional Data Assimilation System] is significantly better than the global reanalysis at capturing the regional hydrological cycle, the diurnal cycle and other important features of weather and climate variability.” Following a 6-yr development and production effort, NCEP's North American Regional Reanalysis (NARR) project was completed in 2004, and data are now available to the scientific community. Along with the use of the NCEP Eta model and its Data Assimilation System (at 32-km–45-layer resolution with 3-hourly output), the hallmarks of the NARR are the incorporation of hourly assimilation of precipitation, which leverages a comprehensive precipitation analysis effort, the use of a recent version of the Noah land surface model, and the use of numerous other datasets that are additional or improv...

The High-Resolution Rapid Refresh (HRRR): An Hourly Updating Convection-Allowing Forecast Model. Part 1: Motivation and System Description

Abstract: The High-Resolution Rapid Refresh (HRRR) is a convection-allowing implementation of the Weather Research and Forecasting model (WRF-ARW) with hourly data assimilation that covers the conterminous United States and Alaska and runs in real time at the NOAA National Centers for Environmental Prediction. Implemented operationally at NOAA/NCEP in 2014, the HRRR features 3-km horizontal grid spacing and frequent forecasts (hourly for CONUS and 3-hourly for Alaska). HRRR initialization is designed for optimal short-range forecast skill with a particular focus on the evolution of precipitating systems. Key components of the initialization are radar-reflectivity data assimilation, hybrid ensemble-variational assimilation of conventional weather observations, and a cloud analysis to initialize stratiform cloud layers. From this initial state, HRRR forecasts are produced out to 18 h every hour, and out to 48 h every 6 h, with boundary conditions provided by the Rapid Refresh system. Between 2014 and 2020, HRRR development was focused on reducing model bias errors and improving forecast realism and accuracy. Improved representation of the planetary boundary layer, subgrid-scale clouds, and land surface contributed extensively to overall HRRR improvements. The final version of the HRRR (HRRRv4), implemented in late 2020, also features hybrid data assimilation using flow-dependent covariances from a 3-km, 36-member ensemble (“HRRRDAS”) with explicit convective storms. HRRRv4 also includes prediction of wildfire smoke plumes. The HRRR provides a baseline capability for evaluating NOAA’s next-generation Rapid Refresh Forecast System, now under development.

The High-Resolution Rapid Refresh (HRRR): An Hourly Updating Convection-Allowing Forecast Model. Part 2: Forecast Performance

Abstract: The High-Resolution Rapid Refresh (HRRR) is a convection-allowing implementation of the Advanced Weather Research and Forecast model (WRF-ARW) that covers the conterminous United States and Alaska and runs hourly (for CONUS; every three hours for Alaska) in real time at the National Centers for Environmental Prediction. The high-resolution forecasts support a variety of user applications including aviation, renewable energy, and prediction of many forms of severe weather. In this second of two articles, forecast performance is documented for a wide variety of forecast variables and across HRRR versions. HRRR performance varies across geographical domain, season, and time of day depending on both prevalence of particular meteorological phenomena and the availability of both conventional and non-conventional observations. Station-based verification of surface weather forecasts (2-m temperature and dewpoint temperature, 10-m winds, visibility, and cloud ceiling) highlights the ability of the HRRR to represent daily planetary boundary layer evolution and the development of convective and stratiform cloud systems, while gridded verification of simulated composite radar reflectivity and quantitative precipitation forecasts reveals HRRR predictive skill for summer and winter precipitation systems. Significant improvements in performance for specific forecast problems are documented for the upgrade versions of the HRRR (HRRRv2, v3, and v4) implemented in 2016, 2018, and 2020, respectively. Development of the HRRR model data assimilation and physics paves the way for future progress with operational convective-scale modeling.

An Assessment of a Real-Time Analysis and Its Impact on Dispersion Modeling

Abstract: The height of the Planetary Boundary Layer (PBL) is an important quantity for certain applications such as dispersion modeling. A dedicated two-dimensional PBL height analysis has been developed as an additional component of NCEP’s Real-Time Mesoscale Analysis. As for other meteorological analysis applications, the quality of the output is dependent on the quality of the input, including the observation. Here we assess the quality and potential for use in the PBL height analysis of a series of candidate observations, including Radiosonde Observations (RAOBS), Aircraft Communications Addressing and Reporting System (ACARS), Cooperative Agency Profilers (CAP), COSMIC satellite Radio Occultation and NWS Next-Rad radar reflectivities. The quality is assessed both by physical plausibility of the measurements and by comparison of the observations and the resulting analysis with independent observations not used in the analysis.

The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2)

Abstract: The Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), is the latest atmospheric reanalysis of the modern satellite era produced by NASA’s Global Modeling and Assimilation Office (GMAO). MERRA-2 assimilates observation types not available to its predecessor, MERRA, and includes updates to the Goddard Earth Observing System (GEOS) model and analysis scheme so as to provide a viable ongoing climate analysis beyond MERRA’s terminus. While addressing known limitations of MERRA, MERRA-2 is also intended to be a development milestone for a future integrated Earth system analysis (IESA) currently under development at GMAO. This paper provides an overview of the MERRA-2 system and various performance metrics. Among the advances in MERRA-2 relevant to IESA are the assimilation of aerosol observations, several improvements to the representation of the stratosphere including ozone, and improved representations of cryospheric processes. Other improvements in the quality of M...

The NCEP Climate Forecast System Reanalysis

Abstract: The NCEP Climate Forecast System Reanalysis (CFSR) was completed for the 31-yr period from 1979 to 2009, in January 2010. The CFSR was designed and executed as a global, high-resolution coupled atmosphere–ocean–land surface–sea ice system to provide the best estimate of the state of these coupled domains over this period. The current CFSR will be extended as an operational, real-time product into the future. New features of the CFSR include 1) coupling of the atmosphere and ocean during the generation of the 6-h guess field, 2) an interactive sea ice model, and 3) assimilation of satellite radiances by the Gridpoint Statistical Interpolation (GSI) scheme over the entire period. The CFSR global atmosphere resolution is ~38 km (T382) with 64 levels extending from the surface to 0.26 hPa. The global ocean's latitudinal spacing is 0.25° at the equator, extending to a global 0.5° beyond the tropics, with 40 levels to a depth of 4737 m. The global land surface model has four soil levels and the global sea ice m...

NOAA’s HYSPLIT Atmospheric Transport and Dispersion Modeling System

Abstract: The Hybrid Single-Particle Lagrangian Integrated Trajectory model (HYSPLIT), developed by NOAA’s Air Resources Laboratory, is one of the most widely used models for atmospheric trajectory and dispersion calculations. We present the model’s historical evolution over the last 30 years from simple hand-drawn back trajectories to very sophisticated computations of transport, mixing, chemical transformation, and deposition of pollutants and hazardous materials. We highlight recent applications of the HYSPLIT modeling system, including the simulation of atmospheric tracer release experiments, radionuclides, smoke originated from wild fires, volcanic ash, mercury, and wind-blown dust.

Precipitation downscaling under climate change: Recent developments to bridge the gap between dynamical models and the end user

Abstract: Precipitation downscaling improves the coarse resolution and poor representation of precipitation in global climate models and helps end users to assess the likely hydrological impacts of climate change. This paper integrates perspectives from meteorologists, climatologists, statisticians, and hydrologists to identify generic end user (in particular, impact modeler) needs and to discuss downscaling capabilities and gaps. End users need a reliable representation of precipitation intensities and temporal and spatial variability, as well as physical consistency, independent of region and season. In addition to presenting dynamical downscaling, we review perfect prognosis statistical downscaling, model output statistics, and weather generators, focusing on recent developments to improve the representation of space-time variability. Furthermore, evaluation techniques to assess downscaling skill are presented. Downscaling adds considerable value to projections from global climate models. Remaining gaps are uncertainties arising from sparse data; representation of extreme summer precipitation, subdaily precipitation, and full precipitation fields on fine scales; capturing changes in small-scale processes and their feedback on large scales; and errors inherited from the driving global climate model.

Indices for monitoring changes in extremes based on daily temperature and precipitation data

Abstract: Indices for climate variability and extremes have been used for a long time, often by assessing days with temperature or precipitation observations above or below specific physically-based thresholds. While these indices provided insight into local conditions, few physically based thresholds have relevance in all parts of the world. Therefore, indices of extremes evolved over time and now often focus on relative thresholds that describe features in the tails of the distributions of meteorological variables. In order to help understand how extremes are changing globally, a subset of the wide range of possible indices is now being coordinated internationally which allows the results of studies from different parts of the world to fit together seamlessly. This paper reviews these as well as other indices of extremes and documents the obstacles to robustly calculating and analyzing indices and the methods developed to overcome these obstacles. Gridding indices are necessary in order to compare observations with climate model output. However, gridding indices from daily data are not always straightforward because averaging daily information from many stations tends to dampen gridded extremes. The paper describes recent progress in attribution of the changes in gridded indices of extremes that demonstrates human influence on the probability of extremes. The paper also describes model projections of the future and wraps up with a discussion of ongoing efforts to refine indices of extremes as they are being readied to contribute to the IPCC's Fifth Assessment Report. WIREs Clim Change 2011, 2:851–870. doi: 10.1002/wcc.147 For further resources related to this article, please visit the WIREs website.