Search Results

You are looking at 1 - 6 of 6 items for :

  • Author or Editor: John Turner x
  • Weather and Forecasting x
  • Refine by Access: All Content x
Clear All Modify Search
Steven Colwell
and
John Turner

Abstract

An assessment is made of the availability of Antarctic synoptic observations on the World Meteorological Organization (WMO) Global Telecommunication System (GTS) during the trial periods (5–9 July 1993 and 1–15 February 1994) and winter and summer special observing periods (SOPs) (July 1994 and January 1995) of the Antarctic First Regional Observing Study of the Troposphere project. The data collected at two nodes of the GTS—Melbourne, Australia, and Bracknell, United Kingdom—are considered. Data received at Melbourne were passed on to the Australian Bureau of Meteorology in Hobart and those received at Bracknell were passed similarly on to Cambridge. The trial periods showed that there were large differences in the number of surface observations received at the two nodes. Although Hobart always received more upper-air data than Cambridge, the reverse was true with automatic weather station (AWS) data. The experience from the SOPs indicates that there are now almost 50% more AWS observations on the GTS than surface observations from the staffed stations.

Full access
Gareth J. Marshall
and
John Turner

Abstract

Using data obtained during January 1995—the third of three special observing periods associated with the Antarctic First Regional Observing Study of the Troposphere project—over a sector of the Southern Ocean (SO), this study investigates the capabilities of European Remote Sensing satellite (ERS) scatterometer winds to portray accurately synoptic-scale weather systems and comments upon their potential contribution to the forecasting process in this region.

A sample population of cyclones was defined using satellite imagery and analyzed charts. The scatterometer successfully “captured” more than 60% of these systems that were existent over the open ocean. For manual analyses, the wind vectors proved extremely good for locating the positions of fronts, apparent as a marked turning in the wind direction, which coincided closely with frontal bands observed in contemporaneous satellite imagery. In most cases the wind vectors were also able to locate cyclone centers: their superior spatial resolution as compared with numerical analysis schemes revealed significant positional errors in the latter. This study demonstrates that typically each cyclone was captured twice by a scatterometer swath: such multitemporal data can provide information on the development of a system through changes in the strength of its associated winds.

Those 40% of systems that were not captured generally had a duration of less than a day and in that time were never encompassed by the scatterometer swath, a limiting factor in the instrument’s effectiveness, as noted by other studies. However, this study reveals that the most significant problem in high southern latitudes appears to be missing data resulting from the use of the operationally mutually exclusive synthetic aperture radar instrument over coastal Antarctica. Additional limitations of scatterometer data for observing synoptic-scale systems are shown to be the maximum and minimum restrictions on the range of wind speeds that can be successfully derived and the granularity problems that are still existent in some ERS data. Nonetheless, scatterometer data have the potential to provide extremely important information for the forecasting process over the data-sparse SO, with the near-surface winds able to give an accurate reflection of the degree of activity of a weather system.

Full access
Xia Sun
,
Dominikus Heinzeller
,
Ligia Bernardet
,
Linlin Pan
,
Weiwei Li
,
David Turner
, and
John Brown

Abstract

Convective available potential energy (CAPE) is an important index for storm forecasting. Recent versions (v15.2 and v16) of the Global Forecast System (GFS) predict lower values of CAPE during summertime in the continental United States than analysis and observation. We conducted an evaluation of the GFS in simulating summertime CAPE using an example from the Unified Forecast System Case Study collection to investigate the factors that lead to the low CAPE bias in GFS. Specifically, we investigated the surface energy budget, soil properties, and near-surface and upper-level meteorological fields. Results show that the GFS simulates smaller surface latent heat flux and larger surface sensible heat flux than the observations. This can be attributed to the slightly drier-than-observed soil moisture in the GFS that comes from an offline global land data assimilation system. The lower simulated CAPE in GFS v16 is related to the early drop of surface net radiation with excessive boundary layer cloud after midday when compared with GFS v15.2. A moisture-budget analysis indicates that errors in the large-scale advection of water vapor does not contribute to the dry bias in the GFS at low levels. Common Community Physics Package single-column model (SCM) experiments suggest that with realistic initial vertical profiles, SCM simulations generate a larger CAPE than runs with GFS IC. SCM runs with an active LSM tend to produce smaller CAPE than that with prescribed surface fluxes. Note that the findings are only applicable to this case study. Including more warm-season cases would enhance the generalizability of our findings.

Significance Statement

Convective available potential energy (CAPE) is one of the key parameters for severe weather analysis. The low bias of CAPE is identified by forecasters as one of the key issues for the NOAA operational global numerical weather prediction model, Global Forecast System (GFS). Our case study shows that the lower CAPE in GFS is related to the drier atmosphere than observed within the lowest 1 km. Further investigations suggest that it is related to the drier atmosphere that already exists in the initial conditions, which are produced by the Global Data Assimilation System, in which an earlier 6-h GFS forecast is combined with current observations. It is also attributed to the slightly lower simulated soil moisture than observed. The lower CAPE in GFS v16 when compared with GFS v15.2 in the case analyzed here is related to excessive boundary layer cloud formation beginning at midday that leads to a drop of net radiation reaching the surface and thus less latent heat feeding back to the low-level atmosphere.

Restricted access
John Turner
,
Steven Leonard
,
Gareth J. Marshall
,
Michael Pook
,
Lance Cowled
,
Richard Jardine
,
Stephen Pendlebury
, and
Neil Adams

Abstract

The quality of the Antarctic operational analyses that were distributed over the Global Telecommunications System during the First Regional Observing Study of the Troposphere project special observing period of July 1994 is considered. Numerical analyses from the U.K. Meteorological Office, the European Centre for Medium-Range Weather Forecasts, the Australian Bureau of Meteorology, and the U.S. National Centers for Environmental Prediction are compared with high quality analyses prepared using all available late data and high-resolution satellite imagery. The subjective assessment of the analyses indicated that no large, synoptic-scale systems were missing, but major discrepancies were found in terms of the depth of the lows, location errors, and failures to resolve the complexities of systems. Generally, the central pressures of the lows were handled better than the locations of the centers. Only 4 lows out of a total of 161 in the Eastern Hemisphere during the period 22–28 July had to be relocated more than 500 km. High-quality satellite imagery was very important in correcting the locations of the lows and in resolving the structure of multicentered systems, which were often found to be much more complex than analyzed on the operational charts. The satellite imagery was of less value over the continent since some of the lows here, which were analyzed using automatic weather station data, had no cloud associated with them as a result of the atmosphere being very dry. Few changes were made to the positions of anticyclones and only minor modifications to ridges were required. The mean pressure at mean sea level fields for July 1994 as produced by the four models were all very similar, but the Australian model stood out as slightly different over the Amundsen Sea because of large differences in the handling on one large low during the early part of the month. The Phillpot technique for the analysis of the 500-hPa surface over the interior of the continent was of particular value in resolving structure in the circulation.

Full access
Eric P. James
,
Curtis R. Alexander
,
David C. Dowell
,
Stephen S. Weygandt
,
Stanley G. Benjamin
,
Geoffrey S. Manikin
,
John M. Brown
,
Joseph B. Olson
,
Ming Hu
,
Tatiana G. Smirnova
,
Terra Ladwig
,
Jaymes S. Kenyon
, and
David D. Turner

Abstract

The High-Resolution Rapid Refresh (HRRR) is a convection-allowing implementation of the Advanced Research version of the Weather Research and Forecast (WRF-ARW) Model that covers the conterminous United States and Alaska and runs hourly (for CONUS; every 3 h 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 nonconventional 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.

Significance Statement

NOAA’s operational hourly updating convection-allowing model, the High-Resolution Rapid Refresh (HRRR), is a key tool for short-range weather forecasting and situational awareness. Improvements in assimilation of weather observations, as well as in physics parameterizations, has led to improvements in simulated radar reflectivity and quantitative precipitation forecasts since the initial implementation of HRRR in September 2014. Other targeted development has focused on improved representation of the diurnal cycle of the planetary boundary layer, resulting in improved near-surface temperature and humidity forecasts. Additional physics and data assimilation changes have led to improved treatment of the development and erosion of low-level clouds, including subgrid-scale clouds. The final version of HRRR features storm-scale ensemble data assimilation and explicit prediction of wildfire smoke plumes.

Open access
David C. Dowell
,
Curtis R. Alexander
,
Eric P. James
,
Stephen S. Weygandt
,
Stanley G. Benjamin
,
Geoffrey S. Manikin
,
Benjamin T. Blake
,
John M. Brown
,
Joseph B. Olson
,
Ming Hu
,
Tatiana G. Smirnova
,
Terra Ladwig
,
Jaymes S. Kenyon
,
Ravan Ahmadov
,
David D. Turner
,
Jeffrey D. Duda
, and
Trevor I. Alcott

Abstract

The High-Resolution Rapid Refresh (HRRR) is a convection-allowing implementation of the Advanced Research version of the Weather Research and Forecasting (WRF-ARW) Model 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 (NCEP). 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.

Significance Statement

NOAA’s operational hourly updating, convection-allowing model, the High-Resolution Rapid Refresh (HRRR), is a key tool for short-range weather forecasting and situational awareness. Improvements in assimilation of weather observations, as well as in physics parameterizations, have led to improvements in simulated radar reflectivity and quantitative precipitation forecasts since the initial implementation of HRRR in September 2014. Other targeted development has focused on improved representation of the diurnal cycle of the planetary boundary layer, resulting in improved near-surface temperature and humidity forecasts. Additional physics and data assimilation changes have led to improved treatment of the development and erosion of low-level clouds, including subgrid-scale clouds. The final version of HRRR features storm-scale ensemble data assimilation and explicit prediction of wildfire smoke plumes.

Open access