# Presentation: 420685

## Gauges, Surface and NWP data

- Working Group summary slides
- June 29, 2005

## How to organize?

- By special emphases/interests
- Basic areas from Steve V.
- By individual data type
- By description of existing ‘systems’
- Some combination

## Aspects of Gauge data? 

- Data types, equipment, communications
- Relative accuracies, reliabilities
- Effective ranges of coverage, densities, issues of scale
- Mesonets
- Quality control strategies, in real-time and in retrospect
- Historical versus real-time (operational) datasets
- Archive availabilities

**Notes:**
Umbrella of models
	pick and choose

## Aspects of Surface data?

- Classic meteorologic synoptic variables at observing stations
- Temperature, moisture, winds, pressure, etc.
- derived, interpreted
- vertical soundings, profiles (observed, remotely-sensed, modeled,?); surface transects; time-height plots; etc.
- climatologic fields
- categorical (targeted) climatologies
- hydrologic observations
- stage & flow observations
- stage mesonets with level loggers
- Availability/consistency of integrated Surface Analyses
- Usage for regime typing and categorization

**Notes:**
Umbrella of models
	pick and choose

## Aspects of NWP data? 

- Particular models, fields, characteristics
- Deterministic, ensembled traces
- Resolutions in time-space
- Direct model usage vs. diagnostic usage
- Utility for observation/forecast/updating
- Downscaling approaches
- Simplified (process) models
- Data assimilation systems
- Rapid refresh models
- Coupled models (e.g., atmospheric-hydrologic)
- Application/Utility-specific requirements for model output

**Notes:**
Umbrella of models
	pick and choose

## Special Emphases

- Hydromet Inputs over mountains for River Forecasting (PP, TA, HZ)
- The utility of hydroclimatology in hydrometeorology
- Fundamental Issues of ‘representative’ scale and resolution
- NWP & surface analyses for regime typing
- Gauge QC methods; manual & auto
- Bias adjustment w/ gauges & r.s./NWP/surface data (& vice versa)
- Oklahoma Mesonets
- Modernizing the Cooperative Network
- Archival & availability; real-time vs reanalysis
- NCDC and real-time/meta- data archiving

## Areas suggested by Steve

- Analysis methods for Gauge data
- current methods
- MM, Oklahoma Mesonet, Neron
- regional approaches
- e.g., as mentioned, in the West, NWS River Forecast Centers (RFC’s) have used systems such as MM, with climatologic scaling based on PRISM (Daly et al, at Oregon State University)
- I know there a number of other regional/local/commercial systems (e.g., P3 in Tulsa?)
- Gauge QC methods
- Use of sfc, r.s., NWP data to identify storm type, characteristics, etc.
- e.g., research to include regime characterization into analysis algorithms and strategies; one example is an ongoing effort at synoptic regime (targeted) versions of the PRISM climatologies by Daly and the Oregon Climate Service with the NWS Western Region.
- How to quantify Gauge uncertainty
- Real-time calibration with Gauge data
**RFC** datasets as real-time components for Q2

**Notes:**
Umbrella of models
	pick and choose

## Ed’s comments on ‘representativeness’

- Does Gauge QC matter?
- Importance of QC for model verification
- ETA 12 hour forecasts
- WRF verification
- Manual datasets and zeros
- Business case for QC as a priority

## Analysis Methods for QC

- Current Methods
- Regional example
- Mountain Mapper

## CNRFC area Topography

## Hydromet Input Development in Complex Terrain

- Common treatment of *Observed* & *Future, R.T & historical*:
- Mean Areal Precipitation distribution (MAP)
- Mean Areal Temperature distribution (MAT)
- Mean Areal Snow/Freezing Level (‘MAZ’)
- Complications
- Extreme Non-stationarity/heterogeneity of fields
- Phase changes with elevation
- Limited effectiveness of remote sensors
- Advantages
- High Consistency across time-space scales
- Good Atmospheric Model performance
- Background Commonality
- Climatology (**PRISM**)

**Notes:**
Umbrella of models
	pick and choose

## Inversions in the West:  data priority pyramid 

- Remote sensors
- Surface datasets
- Atmospheric models
- Climatologies
- Gauges

**Notes:**
Umbrella of models
	pick and choose

## Bottom line:  Basins

## Mountain Mapper (MM) System:  Quantitative Precipitation  Development

**QC-DAILY**:  QP Estimation (QPE) w/ Observed Data
- QC of gauge reports
- neighbor estimations via ‘normalized’ data
- Gridded estimation of MAPs
- RFS ingestion in real-time
- problems with discontiguous areas with MAPX
- Historical analysis/calibration
- Freezing Level Estimation
- Explicit Rain vs Snow  delineation in RFS
- Assist QC of observed data (e.g..., tipping bucket)
**SPECIFY**:  QP Forecast (QPF) specification
**VERIFY**:  QPF/QPE verification
*ITERATE*:  MAP Analysis/Update/Iteration

## Complimentary Data Types in Daily_QC

**Surface Temperature**
- QC of gauge reports
- Normalize/scale reports with climatology
- neighbor estimations with differences (vs ratios with PP)
- Gridded estimation of  Temperature
- PRISM max/min Temperature climatology grids
- Display of basin aggregated MATs
- Implicit Freezing Level Estimation
- Currently:  Lapsed for Rain/Snow delineation in RFS
- Assist QC of observed data (e.g..., tipping bucket)
- Snowmelt energy exchange
**Freezing (Snow) Level heights**
- ‘observations’ from RUC analysis field
- PAC-Jet Profilers
- Simple (flat) Interpolation (no scaling)

## (

**POINTS**
- (stations)

**A**

**C**

**D**

**F**

**B**

**E**

- N

- N

- x

- i

- )

- P

- i

- =

- d= distance of separation
- W= station weight
- N= station seasonal mean
- P= station precip catch

- Estimating **Precipitation** Catch at station with missing report (P  ) from surrounding observations (P  )

- i

- x

- where

## (

**POINTS**

**GRIDS**

**LUMP**

***PATTERN***

***INTEGRATE***

- N

- N

- x

- i

- )

- =

- =

- 1

- d

- 2

- d= distance of separation

- Estimate grid boxes as “stations”

## (

**POINTS**

**GRIDS**

**LUMPS**

***PATTERN***

***INTEGRATE***

- N

- N

- x

- i

- )

- =

- =

- 1

- d

- 2

- d= distance of separation

**For each MAP area**

**different for each season/month**

## *

**POINTS**

**HRAP GRID**

**QPF -----**

**LUMP **(MAP) **------------------**

*****

*****

*****

**Climatology:  **
- Seasonal or
- Monthly Isohyets

## *

**POINTS**

**Climatology:  **
- Seasonal or
- Monthly Isohyets

**HRAP GRID**

**QPF -----**

**LUMP **(MAP) **-----------------**

*****

*****

**Rain/snow**

**20 % SNOW**

**80% RAIN**

**20% MAP into SNOW MODEL**

**80% MAP into SOIL MODEL**

**Results in SMALLER **
**Hydrograph**

**BASIN RUNOFF MODEL**
- (UNIT HYDROGRAPH)

**5500 ft**

**5500 ft**

## E    = E   + (( T   - PXTEMP)  (100/L   ))

**Rain/snow**

**20 % SNOW**

**80% RAIN**

**5500 ft**

**E    = E   + (( T   - PXTEMP)  (100/L   ))**

**p**

**rs**

**v**

**v**

*****

- E   = rain/snow elevation
- E   = input variable elevation:  freezing level or MAT
- T   = input variable temperature:  0  C or MAT
- PXTEMP = threshold temperature [1 to 2  C]
- L   = lapse rate during precipitation (  C/100m)
-         [wet adiabatic or approx  .55  C/100m or 3  F/1000 ft]

- p

- rs

- v

- v

- o

- o

- o

- o

- o

*Rain/snow elevation approximately 1000 feet below 0  C level (here, assume ****1500**** feet)*

- o

## (

**POINTS**
- (stations)

**A**

**C**

**D**

**F**

**B**

**E**

- N

- x

- )

- T

- x

- T

- i

- =

- =

- 1

- d

- d= distance of separation
- W= station weight
- N= station monthly mean
- T= station Temperature
- Fe= elevation factor
- E= elevation

- Estimating** Temperature **at a station with a missing report (T  ) from surrounding observations (T  )

- where

- +

- -

- )

- *

- +

- Fe

- E

- *

- ix

- x

- ix

- x

- i

## Works well but Obstacles include:

- Timeliness:
- Computational Speed
- Communications
- Human interactions
- Software Tools
- Non-orographic events
- Once you’ve got the hammer, everything looks like a nail

## Baseline is Improving:ongoing enhancements

- Targeted (non-static) backgrounds for normalization
- E.g., Ongoing development of **Smart-PRISM** (regime-sensitive) climatologies (Daly, Taylor group at Oregon State)
- Atmospheric/process model data inclusion
- Higher Spatial/Temporal resolutions
- Iterative updating
- Increased automation

## Gauge QC methods/tests 

- Gross range checks
- Nearest neighbor estimation (x-validation)
- Gage history
- Visual inspection of accumulation trace
- Measuring Hardware: tipping vs. weighing; shielded?
- Station network characteristics (SNOTEL, ASOS, etc.)
- Station Measuring Hardware: tipping vs. weighing
- Location wrt Freezing (Snow) Level
- Disqualify tipping buckets

**Notes:**
Umbrella of models
	pick and choose

## More Gauge QC methods/tests 

- Compare w/ SWE increment at co-located pillow (substitute)
- Compare with remotely sensed data
- NEXRAD overlay
- IR Satellite views
- Lightning strikes
- Consider synoptic regime and surface data (e.g., post frontal convection?)
- Adjust deviation allowed from neighbor estimation
- Reasonableness of Gridded (hrap)/Basin (MAP) renditions of data
- Consult longer duration results (monthly/seasonal performance w/ monthly-qc)
- Consistency with temperature, freezing level, **soil moisture**

**Notes:**
Umbrella of models
	pick and choose

## Identification of Synoptic Regime

- Automated production from NWP/R.S./surface data
- Adequate skill?
- At what resolutions
- Hourly
- 5 km
- Recommendations for prototyping

## Quantifying Gage Uncertainty

- Research topic
- Indications from operational archive of meta-data
- Results from multiple tests
- Utility of Hydrologic application

## Real-time role for RFC’s/mesonets in Q2

- Gauge QC stream as output
- 6/24 hourly
- 1 hourly progress
- Availabilities
- Real-time
- Historical
- Meta-data on QC
- Validation, Bias adjustment
- Regional Variations/Optimizations

## Platform & Tools for ‘Community’ Model Development?

- Accessible
- Extensible
- Consistent