Alaska Ceiling and Visibility Analysis (CVA-AK) Product

Project Tabs - CVA-AK


Poor weather conditions, particularly restricted visibility and low cloud tops, were the leading cause of fatal general aviation (GA) accidents in Alaska from 2001-2012.  Traditional weather observations from Alaska’s widely dispersed airfields inadequately forewarn of weather likely to be encountered along routes between stations or, in particular, through hazardous mountain passes with localized conditions.  In 2014, the National Transportation Safety Board (NTSB) included “GENERAL AVIATION: IDENTIFY and COMMUNICATE HAZARDOUS WEATHER” on its Most Wanted List to improve transportation safety.

NCAR Flight Category analysis field based on ceiling and visibility for Alaska on 15 August 2016.  LFR: dark gray, IFR: light gray, MVFR: yellow, VFR: green. METAR C&V observations overlaid.

The NCAR/RAL, the MIT/LL, the Alaska Aviation Weather Unit (AAWU), and the FAA are involved in a three year collaborative effort to produce a rapidly-updated, high resolution, gridded product of ceiling and visibility (C&V) conditions across Alaska.  This product, known as the Ceiling and Visibility Analysis – Alaska (CVA-AK) serves as a “first guess” estimate of C&V conditions across Alaska at or near instrumented and non-instrumented airfields and along data-sparse routes between airfields including treacherous and heavily-traveled mountain passes.

Visible satellite image for Alaska on 15 August 2016.  METAR station reports for ceiling (cyan), visibility (magenta) and current weather (yellow) are overlaid.

The CVA-AK product combines ceiling and visibility information from the latest NCEP RAP model with METAR observations of C & V using data fusion techniques to produce Flight Category, Ceiling and Visibility gridded fields. These fields are updated every 20 min and hourly analysis products are viewable by AAWU forecasters on the IC4D display system that they use to produce their aviation forecasts.

Future versions of the CVA-AK product will include geostationary and polar orbiter satellite observations and visibility estimates retrieved from the FAA web camera imagery collected in Alaska and output from the Alaska HRRR model, when it becomes available.




Rita Roberts

Jim Cowie

James Pinto

Tressa Fowler

Dan Megenhardt

 MIT Lincoln Laboratories

Robert Hallowell

Michael Matthews

Federal Aviation Administration

Jenny Colavito

Steve Abelman

 Alaska Aviation Weather Unit

Don Moore – MIC

Doug Wesley – SOO

Jeff Cotterman – IT specialist

Forecast Staff

AAWU Home page

Relevant Publications

Herzegh, P., G. Wiener, R. Bateman, J. Cowie, and J. Black, 2015: Data fusion enables better recognition of ceiling and visibility hazards in aviation.  Bull. Amer. Meteor. Soc., 96, 526-532.

Blended Product


Three versions of a blended CVA-AK product are planned. 

Version 1 -Blend of RAP NWP model with METAR (ASOS and AWOS) surface stations

Left panel, ceiling. middle panel, visibility. right panel, flight category (click on image to enlarge)

An initial version of the CVA-AK was installed at the AAWU in May 2016.  The 2 h forecast of ceiling and visibility from the 11 km RAP model forms the base layer. METAR ceiling and visibility observations are ingested and compared to the RAP values at those sites, showing agreement or disagreement with the model.  CVA-AK products are interpolated to a 6 km AK National Digital Forecast Database (NDFD) grid. Ceiling, visibility and flight category products are updated every 20 min with most recent METAR reports, but only the output on the hour are currently displayed on the AAWU forecasters’ IC4D display system.

Updated Version 1

An updated Version 1 product suite will expand the use of model and METAR observations in the blending algorithms. The following steps will be included in blending the model with METARs observations.

For each grid point, use model data when:

  • Closest METAR is farther than a pre-defined distance (e.g. 20 km)
  • If terrain difference between closest METAR and grid point is above a certain threshold.
  • If grid point is over water
  • If model value has worse C&V condition

Else, use METAR value at the grid point.

Version 2 - Satellite products will be included into version 2 of the CVA-AK product using the GOES-West, GOES-R, and Himawari (as available) datasets

The Jedlovec cloud mask will be run on GOES data to identify cloud/no cloud grid points and this information will be included in the blended CVA-AK product. Polar orbiter data from the Suomi NPP VIIRS and the AVHRR are being ingested for Alaska and will be evaluated for inclusion in the CVA-AK.   The RAP model will be replaced with the Alaska HRRR model when it becomes available.

Version 3 –FAA web camera observations will be incorporated into the CVA-AK to improve the product.

Top Panel, FAA Web camera locations in Alaska. Red shaded polygons highlight camera imagery showing reduced visibility or reduced cloud ceilings on 4 October 2016. Bottom Panel, web camera imagery to the southwest on 4 October 2016 from McKinley North site.  Imagery on a clear day and camera viewing angles are also shown.

MIT/LL is in the process of refining an algorithm to retrieve visibility estimates at selected camera locations. Ultimately, this algorithm will be scaled to process visibility retrievals at all of the Alaska web camera sites and this input will be included into version 3 of the CVA-AK.

Comparative Analysis

Because of the sparseness of surface observing stations in Alaska that measure C&V, we must use very-short range forecasts from the RAP model to fill in between reporting stations when generating the C&V analysis product. Understanding the level of consistency between surface-based observations and RAP forecasts of C&V is critical for developing a useful analyses product. One aspect of the C&V forecasts we have been studying is their ability to represent observed long-term trends. The Figure shown below provides a comparison of the frequency of occurrence of ceiling heights of 3000 ft or less across the state of Alaska for July and August 2016. The surface observations are shown as small (~30 km) tiles in the top panels and the RAP 2hour forecast data values are shown in the middle panels as much larger tiles. A number of distinct regimes are evident in both observations and the RAP model data including (1) greater frequency of low ceilings along coastal regions, (2) minima in low ceiling frequency in the Anchorage area and (3) increased frequency of low ceilings across the North Slope in August. The model provides much greater detail in terms of resolving coastal and terrain driven gradients in the frequency of low ceilings, but notable biases in the model predictions are also evident. These differences become more clear when looking at frequency difference (modeled minus observed frequency) plots shown in the bottom panels. In both months, the RAP model shows a consistent over forecasting bias in the center of Alaska and an underforecasting bias in the southwest. Also interesting is the gradient seen across some of the coastal tiles (especially along the North Slope in July) where the variation in bias across the tile is due in part to the lack of representativeness of the observation extended over the coastal waters. These comparisons can be used to remove first order bias in the model and to better understand the representativeness of the surface observation measurements and how this information should be spread in the gridded C&V analysis product.

A comparison of the frequency of occurrence of ceiling heights of 3000 ft or less across the state of Alaska for July and August 2016

Frequency difference (modeled minus observed frequency) plots.

Model-METAR Errors

Top panel: Daily selection menu; Middle panel: Station selection menu; Bottom panel: C&V difference plots and corresponding METAR observations. (click images to enlarge)

RAP model ceiling and visibility estimates are compared to METAR measurements collected every 20 min at Alaska METAR sites.   Daily and weekly error plots (offsets between RAP and METAR values) of ceiling and visibility estimates are being generated automatically. The offset values are calculated as Model minus METARS.  Negative values show where the model is under estimating the ceiling and/or visibility. Daily plots and weekly plots are produced daily and can be accessed at the following links.  Weekly plots contain that day and the 6 days prior to it.

For reference, the figures above show the locations and the 3 and 4 letter identifiers for the Alaska METAR stations. (click images to enlarge)

Clicking on the link for the daily plots will pop up the menu, shown in the top panel to the left.  Selecting day 20160927 pops up the middle panel for selection of a specific METAR station.  Plotted in the bottom panel are differences (offsets) between Model and METAR values at a particular METAR location. 

Threshold values are used to color code the station names. Station names are green if at least 70% of the values are within the thresholds and red if its less than 70%. The current thresholds are 10,000 ft for ceiling height and 5 nautical miles for visibility.  These values are also marked on the plots (along with the 0 line) as colored dashed lines. Station names will be black if both the ceiling and visibility data has fewer than 10% of the expected data points or if all the data points are zero. A light gray is used if only one of the two fields has this condition. So the black and gray colors are meant to show plots that have data issues.

Product Evaluation

Map showing the six Alaska ceiling and visibility verification regions (colored boundaries) overlayed on the aviation icing forecast areas (white boundaries)

Verification of the Alaska ceiling and visibility product will provide baseline measures of performance for the first period of the project. During the second period, the evaluation will focus on product improvement over the preliminary version. Additionally, it will document the nature of the product errors and ability of the product to discriminate between different flight conditions, particularly with respect to differences among the six regions shown in the graphic. Observed conditions come from METARs and pilots’ reports, with a cross-validation approach used to ensure independence. These evaluations will inform decision makers and product improvement efforts.