# Presentation: 318161

## GRIDDED MOSTECHNIQUES, STATUS, AND PLANS

** ****Bob Glahn**
**J. Paul Dallavalle**
**18****th**** Conference on Probability and Statistics in the Atmospheric Sciences**
**Atlanta 2006**

## Definition

**	****MOS:**
**	****A statistical interpretation of model output**
** ****	****	****in terms of (surface) weather**
**	****Relates observations of a weather element ****	****	****to be predicted (predictand) to ****	****	****	****appropriate variables (predictors) via ****	****	****a statistical method**

## Statistical Interpretation

**	****Statistical interpretation can be by any **
**	****	****method desired (e.g. , regression, **
**	****	****discriminant analysis, etc.)**
**	****Predictors include:**
**	****	****NWP model output**
**	****	****Initial observations (persistence)**
**	****	****Geoclimatic data – terrain, normals, etc.**
**	****Predominant method in NWS MOS is multiple **
**                      ****regression**
**	****	****Mathematically simple, easy to implement**
**	****	****Models non-linearity through predictor **
**	****        ****transformations**

## MOS Development

**Uses record of observations at forecast points and model output interpolated to observation locations**
**Applies equations to future run of similar forecast model**
**Can produce probability forecasts from a single run of the underlying NWP model**
**Regression Estimation of Event probabilities (REEP)**

## NWS MOS SYSTEM

**Began in 1969 with distribution of three weather elements at 79 locations over the Eastern US**
**First nationwide graphic product introduced in 1972 produced from 200 point Probability of Precipitation (PoP) forecasts**
**Grew over the years into complete packages encompassing most surface weather variables from several NMC/NCEP numerical models for all US states, Puerto Rico, and Guam for several thousand sites**

## MOS Text Bulletin

**BALTIMORE WASHINGTON INTERNATIONAL          **
** ****KBWI   GFS MOS GUIDANCE   11/19/2004  1200 UTC                      **
** ****DT /NOV  19/NOV  20                /NOV  21                /NOV  22 **
** ****HR   18 21 00 03 06 09 12 15 18 21 00 03 06 09 12 15 18 21 00 06 12 **
** ****N/X                    49          58          48          64    42 **
** ****TMP  58 57 54 52 52 52 52 54 56 56 54 53 53 52 51 58 62 61 54 48 44 **
** ****DPT  51 51 51 50 51 52 52 52 52 52 53 52 51 50 49 50 49 47 47 40 38 **
** ****CLD  OV OV OV OV OV OV OV OV OV OV OV OV OV BK BK BK BK BK SC FW BK **
** ****WDR  36 06 09 09 08 09 09 11 13 13 17 00 28 29 29 31 30 30 30 31 31 **
** ****WSP  01 02 01 01 02 03 04 03 02 02 01 00 02 02 04 07 09 07 04 05 05 **
** ****P06        44    57    48    34    38     4     6     2     1  1  5 **
** ****P12                    63          40          10           2     5 **
** ****Q06         1     1     1     1     1     0     0     0     0  0  0 **
** ****Q12                     1           0           0           0     0 **
** ****T06      2/ 8  5/ 0  2/ 0  0/ 0  0/13  0/ 0  0/ 0  0/ 0  1/14  0/ 0 **
** ****T12            5/ 8        2/ 0        1/14        0/ 0     1/15    **
** ****POZ   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 **
** ****POS   0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 **
** ****TYP   R  R  R  R  R  R  R  R  R  R  R  R  R  R  R  R  R  R  R  R  R **
** ****SNW                     0                       0                 0 **
** ****CIG   7  6  6  5  3  3  3  3  3  3  4  4  5  6  8  6  6  7  8  8  8 **
** ****VIS   6  6  6  5  5  3  3  4  5  5  5  5  5  5  2  7  7  7  7  7  7 **
** ****OBV   N  N  N BR BR BR BR BR BR BR BR BR BR FG FG  N  N  N  N  N  N**

## Traditional MOS Graphics

## Revolution

**Definition:**
**	****A radical change of circumstances in a ****	****scientific, social, or industrial system**
**	****	****(Webster's Dictionary, 1974)**
**National Digital Forecast Database (NDFD)**
**	****Revolutionized the way the NWS produces**
**	****and disseminates its forecasts**
**Interactive Forecast Preparation System (IFPS) was        ****	****enabling technology**

## NDFD

**Definition:**
**	****A database that is a 4-dimensional representation**
**   ****of the weather from the current time to several**
**   ****days into the  future**
**	****   ****-- Vertical dimension not yet well developed**
**   ****Currently, the representation is on a grid of 5-km **
**   ****or so resolution**
**      **
**	****Built from local digital forecast  databases that**
**   ****are updated  as often as meteorological**
**   ****conditions warrant**
**   **

## NDFD Maximum Temperature

## Gridded MOS

**With the NWS mini-modernization of “going digital,” MOS guidance became needed on a grid  commensurate with the resolution being used by local forecasters in producing their local grids**
**MDL has started to produce such grids**

## Objectives

**Produce MOS guidance on high-resolution grid (2.5 to 5 km spacing)**
**Provide with sufficient detail for forecast grid initialization at WFOs**
**Provide with a level of accuracy comparable to that of the station-oriented guidance**

## Gridded MOS Methods

**There are two basic methods of producing Gridded MOS**
**Develop regression equations that can be applied at gridpoints, and directly make forecasts there**
**Develop regression equations that apply to observation sites (single station equations), and grid them (interpolate from quasi-random points to a regular grid)**

## Applying Equations to Gridpoints

**Since observations for most predictands do not exist at gridpoints, a Regional Operator approach has to be used**
**One equation (for a weather element and projection) is developed from pooling the data (observations) in an area (Region)**
**Apply that equation at any and all points within that Region****	**
**Equation will not capture all the local climatology of the stations, but predictors like elevation and climatic variables help**

**Notes:**
What we’re doing........

## Applying Equations to Gridpoints

**Some predictands have surrogates on a grid that can be used for direct gridpoint development**
**Radar data for precipitation**
**Satellite data for clouds**
**Development still usually needs to be done on a regional basis**

**Notes:**
What we’re doing........

## Challenges with Regional Approach

**Difficult to achieve an acceptable level of accuracy**
**Detailed conditional climatology that can be built into single station equations is not well known at gridpoints, and has to be estimated from geoclimatic variables**
**	**
**Boundaries between the regions may exhibit discontinuities **
**Discontinuities can be eliminated by using only one Region (Generalized Operator approach)**
**Generalized Operator equations are even less accurate than Regional**

**Notes:**
What we’re doing........

## Challenges with Single Station Approach

**Objective analysis (gridding the point values) has to be able to estimate major differences of the forecast variable between the forecast data points**
**Such differences vary by forecast variable and are in general not known**
**Such differences vary by time of day, season, and synoptic situation**

## Western CONUS

## Diverse Observational Systems

** ****METAR **
** ****Buoys/C-MAN**
** ****MesoWest (RAWS/SNOTEL)**
** ****NOAA cooperative observer network**
** ****RFC-supplied sites**

## Western CONUS

## Single Station with Gridding Approach Chosen for Temperature and Dew Point Guidance

**Regional approach did not give detail needed in rugged terrain**
**Objective analysis with a lapse rate calculated on-the-fly gives desired detail**

## BCDG Analysis

** ****Method of successive corrections**
** ****Most important distinctions from “standard”**
**   ****successive correction method (currently):**
**Land/water gridpoints treated differently **
**Elevation (“lapse rate”) adjustment**
**Lapse rate calculated on-the-fly**

## Land/Water Distinction

**Only land (or both) datapoints can affect land gridpoints**
**Each gridpoint is designated as land or water**
**Only water (or both) datapoints can affect water gridpoints**
**Each data point is designated as land or water**
**Some are designated as both**

## Land/Water Distinction

**Radius of influence over water 3.5 times that over land to accommodate the sparse buoy data points **
**Small lakes cannot be dealt with unless there is a water datapoint close enough to influence it**
**Interpolation considers land/water distinction**

## Lapse Rate Calculated For Each Station

**Pre-processing step determines 60-100 neighbors for each station**
**Lapse Rate =**
**	****Sum of (temp differences of higher elevation station – lower elevation station)**
**Divided by**
** ****	****sum of absolute difference of elevation of the two stations**
**Normally the lapse rate is negative, but is sometimes positive, especially along the west coast**

## BCDG Analysis Options

** ****First guess can be:**
**Average of all data to be analyzed**
**A specified constant**
**Some desired forecast grid, such as a grid produced from Generalized Operator Equations**
**Number of passes**
**Radius of influence by pass and first guess used**
**Acceptance Criteria by pass and first guess used**
**Buddy Check before discarding**

## BCDG Analysis Options (Cont.)

**Mesh length per pass and first guess used**
**Three possible types of correction per pass and first guess used**
**Amount of correction for a datum based on quality of data source**
**Unusual lapse rates treated differently from “normal” or expected lapse rates**
**Amount of correction can be weighted by distance from gridpoint**
**Radius of influence can be limited**

## BCDG Analysis Options (Cont.)

**Smoothing can vary by pass and first guess option used**
**Special “terrain-following” smoother**
**Smoothes over a 5- or 9-point stencil when the terrain is relatively flat.**
**Does not smooth a gridpoint that is at a high or low point in elevation.**
**Smoothes along contours when a series of three in any of 8 directions are at somewhat the same elevation**

## BCDG Analysis Options (Cont.)

**After last pass, closest gridpoint to a datum can be set to, or nudged toward, that datum**
**Nudging allows a slightly closer fit to the data without creating bulls eyes when a graphic is produced**
**Setting to the value allows an application using the grid to almost always recover the datum**

## Determining the Quality of Grids of Forecasts and Guidance

**Basically two ways:**
**Compute error statistics (e.g., MAE) at datum locations or at gridpoints**
**After gridding, interpolation into the grid can provide point values to compare with observations **
**If a suitable analysis of verifying observations exists, error statistics can be computed at gridpoints.**
**Viewing the graphics for meteorological content**
**Since graphics are many times the method of dissemination and use, this may be of as much importance as the computed error statistics.**

## Determining the Quality of Grids of Forecasts and Guidance (Cont.)

**Withheld Data Tests**
**Data used in the analysis can be fit to less than one degree Fahrenheit .**
**Data not used in the analysis can be fit to about 3 degrees Fahrenheit .**
**Quality of grids**
**Appear to be meteorologically realistic**
**Fine scale detail, especially in data sparse regions, depends on the calculated lapse rates**

## Guidance Grids Being Produced from NCEP’s GFS Model Twice Per Day

**Temperature at 3-hourly intervals**
**Dewpoint at 3-hourly intervals**
**Daytime maximum temperature**
**Nighttime minimum temperature**

## MOS Temperature Analysis (w. terrain and land/water distinction)

## MOS Temperature Analysis (no terrain or land/water distinction)

## MOS Max Temperature Forecast

## NDFD Max Temperature Forecast

## Future

***Expand to other weather elements and to***
***  ******the whole United States***
**Use as much mesonet data as possible**
***Develop BCDG to handle other weather ***
***elements***
**First guess and dependence on topography will vary with element**
***Continue evaluation and improvement***
**Get feedback from forecasters**
**NWS Western Region has begun to look at the grids**