# Presentation: 522345

## Snow Data Issues and NSA Model Assimilation

**Carrie Olheiser**

**National**** ****Operational Hydrologic Remote Sensing Center**

*Office of Climate, Water, and Weather Services*
- National Weather Service, NOAA
- U.S. Department of Commerce

## Snow Observations 
Importance of snow observations to NSA assimilation. 
Data collection, metadata issues, and quality control of snow observations.
National Snow Analyses (NSA)
Snow modeling and data assimilation system for U.S. 
Overview of the data assimilation process. 

- Importance of snow observations to NSA assimilation.
- Data collection, metadata issues, and quality control of snow observations.
**National Snow Analyses (NSA)**
- Snow modeling and data assimilation system for U.S.
- Overview of the data assimilation process.

**Outline**

## Where do snow observations come from?

**February 6, 2004**
**11623 snow depth reports from 4198 unique stations**
**9201 snow water equivalent reports**
**from 968 unique stations**

**March 1, 2004**
**10939 snow depth reports from 3979 unique stations**
**9285 snow water equivalent reports**
**from 1302 unique stations**

**Average day ~ 20,000 stations report any physical element**

**     ****Data Feeds**
-            NoaaPort
-            MADIS
**Regional Surveys**
- Maine Cooperative Snow Survey
- USACE New England District
- Saint Johns River Basin
- Milk River Basin, MT

## Past season           ~ 4000 stations reported SWE.
Average day          ~ 750 stations report SWE
Of these 750          ~ 500 are SNOTEL 

The remaining 250 observations come from a set of ~ 3000 stations.

- Average day          ~ 750 stations report SWE
- Of these 750          ~ 500 are SNOTEL
- The remaining 250 observations come from a set of ~ 3000 stations.

## Slide 5

## Slide 6

## Slide 7

## Metadata Sources at NOHRSC

**Metadata Sources at NOHRSC**
**Over 40 different sources used for station Metadata**

**Weather Forecast Offices, River Forecast Centers and  Regional Offices **

_**Federal and State Agencies**_
**NRCS SNOTEL and Snow Courses**
**USACE New England District Snow Surveys **
**Federal Aviation Administration**
**California Department of Water Resources**
**Maine Cooperative Snow Survey**
**MesoWest ( 150 + smaller mesonets)**
**Numerous State Mesonets**

_**National Weather Services Database**_
**NWSLI**
**CSSA (B44’s)**
**Meteorological Station Location Information**
**NWS-ICAO**
**NWS-METAR**
**MADIS-FSL**
**Hydromet. Automated Data System**
**NCDC**

**Over 50,000 Stations in NOHRSC’s Database **

**NEED ONE-STOP SHOPPING FOR STATION METADATA**

## Stations Without Metadata

- 1950 stations sent observations across NOAAPort with unknown metadata from      January 1, 2004 to August 1, 2004.
- 2,451,864 observations were lost for the unknown1950 stations.

## Importance of Accurate Metadata 

- Numerous databases leads to uncertainties in the station metadata
- Example : Cole Canyon, station CLCW4
- Latitude and Longitude from NWLSI places this station in Canada, it should be in Wyoming
- Snotel Metadata
- 44.80000
- -104.0667
- Elevation 5910 meters
- NWSLI Metadata
- 49.4889
- -104.4161
- Elevation 5910 meters

## Importance of Data in SHEF

- If data is not sent across NOAAPort in SHEF format it falls on floor.
- Many reports are lost in Public Information Statements and Local Storm Reports.
- Some offices send PNS or LSR products as RR products as well.
- Use stranger station format to send data from infrequent reports.

- NOUS45 KSLC 121745
- PNSSLC
- PUBLIC INFORMATION STATEMENT...PRECIP TOTALS
- NATIONAL WEATHER SERVICE SALT LAKE CITY UT
- 1030 AM MST FRI NOV 12 2004
- ...PRELIMINARY STORM TOTALS...
- ANOTHER UPPER LEVEL LOW PRODUCED A MOIST EASTERLY FLOW
- BROUGHT PRECIPITATION TO MOST THE REGION.
- HERE IS A LIST OF TOTALS SINCE WEDNESDAY NIGHT.
- LOCATION                  PRECIPITATION         SNOWFALL
-                                            (INCHES)             (INCHES)
- ...WASATCH MOUNTAINS AND PLATEAU...
- SNOWBASIN MID BOWL               0.54                    6
- FARMINGTON (8000 FT)               0.40                    5
- ROCKY BASIN (OQUIRRHS)         0.40                    4
- BEN LOMOND PEAK (8000 FT)     0.40                    4
- INDIAN CANYON (9100 FT            0.40                    4
- WHITE RIVER (8500 FT)                0.40                    4
- SUNDANCE   (7500 FT)                 0.36                    3
- RED PINE RIDGE (9200 FT)          0.30                    4
- CLEAR CREEK (9200 FT)              0.30                    4
- TIMPANOGOS DIVIDE (8199 FT)  0.30                    3
- CASCADE MOUNTAIN (7800 FT)  0.30                    2
-  HORSE RIDGE (8500 FT)             0.32                    3
- TONY GROVE                                0.30                    3
- ALTA COLLINS                               0.20                    3

## Slide 12

## Importance of Accurate Measurements

**Importance of Accurate Measurements**

## Slide 14

## Meteorological Handbook No. 1, Surface Weather Observations and Reports (FCM-H1-1995).

- Paragraph 12.7.2, a. Precipitation, (d) Snow Depth on Ground (4/sss).  At designated stations, the total snow depth on the ground group shall be coded in the 0000 and 1200 UTC observation whenever there is more than a trace of snow on the ground.  It shall be coded in the 0600 and 1800 UTC observation if there is more than a trace of snow on the ground and more than a trace of precipitation (water equivalent) has occurred within the past 6 hours.  The remark shall be coded in the format, 4/sss, where 4/ is the group indicator and sss is the snow depth in whole inches using three digits.  For example, a snow depth of 21 inches shall be coded as "4/021".The NWS requests the above paragraph be changed to:At designated stations, the total depth of snow on the ground shall be coded in the 0000, 0600, 1200, and 1800 UTC observation whenever there is more than a trace of snow on the ground.  The remark shall be coded in the format, 4/sss, where 4/ is the group indicator and sss is the snow depth in whole inches using three digits.  For example, a snow depth of 21 inches shall be coded as "4/021".

## National Weather Service Observing Handbook No.7, Part IV, Supplementary Observations

**Estimating snow water equivalent using 10 to 1 ratios or lookup tables is *****NOT NWS***** policy**.
- 	( Data is more than worthless)
- Revisions have been made this past summer.
- The new manual is NWSM 10-1311, Supplementary Observations
- http://www.nws.noaa.gov/directives/010/pd01013011a.pdf

## 
Importance of Snow  Observations to the National Snow Analyses (NSA)
Snow modeling and data assimilation system for U.S.

**Importance of Snow  Observations to the National Snow Analyses (NSA)**
- Snow modeling and data assimilation system for U.S.

## Snow Observation Assimilation

**Deltas (Observed – Modeled)**

**12/4/02 12Z to 12/5/02 6Z**

**Deltas between observed and modeled states are examined**
*Coherent spatial pattern is required to warrant update*
- Subgrid variability
**If pattern is explainable, update field is generated and used to nudge the model toward observed states **

***Daily SWE and Snow Depth Observations are used to update the model ***

## Why Assimilate?

- Uncertainties in driving data
- RUC2 precipitation underestimation
- Typing issue; rain/ snow
- Placement of storm track
- Uncertainties due model physics
- Melt problems due to temperature bias
- Sublimation rates

## RUC2 Underestimated Precipitation

## Placement of Storm Track

**Model Over-Estimation**

**Model Under-Estimation**

**The model propagated the system through the region too slowly.**

**Placement of Storm Track**

## Data Sources Used to Determine  Assimilation Region

- Use Current Observations
- Ground Based Snow Depth
- Ground Based Water Equivalent
- Ground Based Snow Density
- Airborne Gamma Data
- Satellite Snow Cover
- 1km NOHRSC Snow Map
- 5km NESDIS Snow Map
- Snow Model Snow Cover
- Present Weather
- Temperature
- Precipitation
- Model Bias
- Typing of precipitation
- Temperature bias

## (Observed – Modeled) Snow StatesObservations collected from previous 24 hours ending at 12Z of current day

**(Observed – Modeled) Snow States****Observations collected from previous 24 hours ending at 12Z of current day**

**2000 to 3000 Snow Depth or Water Equivalent Ground Observations**

## Determination of Assimilation Region Satellite Snow Cover

**Determination of Assimilation Region**** ****Satellite Snow Cover**

- February 10, 2004 Satellite Snow Map

## Determination of Assimilation Region Present Weather

- Melt

- Snow Precipitation

- Non-Snow Precipitation

**Determination of Assimilation Region ****Present Weather**

## Quality Control of Data

- All observations go through automated quality control
- Outlier observations are manually quality control
- Snow data quality control issues
- Station instability
- Spatial representativeness of observation; point-in-pixel consistency
- Fundamental measurement errors

## Manual Quality Control Temporal Consistency 

**Unstable snow pillow observations in early and late season when SWE is less than 2 inches water.**

**Manual Quality Control**** ****Temporal Consistency**** **

## Manual Quality Control Internal Consistency

**Manual Quality Control**** ****Internal Consistency**

**Snow density checks**

***Model:***
***93% Forest Canopy Density***
***Cool Broadleaf Forest***

***Spatial Representativeness: Point-in-Pixel Problem***

- Mt. Mansfield SCAN Site
- - Vermont

## Spatial Representativeness: Point-in-Pixel Problem

***Spatial Representativeness: Point-in-Pixel Problem***

***Model:***
***41% Forest Canopy Density***
***Cool Conifer Forest***

- Virginia Lakes Ridge SNOTEL
- - Sierra Nevada, California

## Leavitt Meadows SNOTEL

- - Sierra Nevada, California

***Spatial Representativeness: Point-in-Pixel Problem***

***Model:***
***47% Forest Canopy Density***
***Cool Conifer Forest***

## Rocky Boy SNOTEL

- - Central Montana

***Spatial Representativeness: Point-in-Pixel Problem***

***Model:***
***40% Forest Canopy Density***
***Hot Irrigated Cropland***

## Glacial Ridge SCAN Site

- - Central Minnesota

***Spatial Representativeness: Point-in-Pixel Problem***

***Model:***
***30% Forest Canopy Density***
***Cool Forest and Field***

## 3.8 cm

***SJ Caribou Snow Course***

***KCAR***

- 10 cm

***Spatial Representativeness***

- 2 stations at the same latitude and longitude

## Generate Nudging Layer

- Methods
- Vertical and Horizontal Distance Weighted
- Horizontal Distance Weighted

**Removing SWE**

**Adding SWE**

## Results

**Removed SWE**

**Added SWE**

- February 4, 2004

- February 5, 2004

- Before Assimilation

- After Assimilation

## Recommendations

**Make accurate, representative snow measurements.**
**Report snow depth with all snow water equivalent measurements.**
**Do not divide snow depth by 10 to infer snow water equivalent; it’s worse than useless.**
**Code all snow data from all U.S. and Canadian reporting stations in SHEF and send to AWIPS.**
**Report time of snow observations; otherwise, 1200 UTC is the system default.**
**Ensure that the correct units are reported in SHEF for each observation; otherwise, English units are the system default.**

## Recommendations

**Send snow data (in SHEF) to AWIPS as soon as possible, ideally within 24  hours after observation.**
**Use appropriate AWIPS headers for all SHEF snow data.**
**Check NWSLI to ensure that all reporting stations are in NWSLI.**
**Check NWSLI to ensure that all lat/long metadata in NWSLI are correct and reported to 4 decimal points for all snow reporting stations.**
**If available, provide digital photographs of each snow course location, estimate of percent forest cover, forest type, and canopy closure.**

## Questions ?www.nohrsc.noaa.gov