# Presentation: 680075

## N. American Regional Reanalysis

_**Land Surface**_**:**
**Configuration, Physics, Results, Cautions**

**NCEP****: *****Where America's Climate and Weather Services Begin***

***Regional Reanalysis Workshop, AMS Meetings, 11 Jan 05***

**Ken Mitchell**
**Mike Ek, Ying Lin, Eric Rogers, P. Shafran, D. Jovic, R. Yang**
**W. Higgins, W. Shi, Y. Fan, J. Janowiak, E. Yarosh**

**NCEP/ EMC, NCEP/CPC**

##  Land Modeling Support at NCEP

**NOAA Office of Global Programs (OGP)**
- GAPP: GEWEX America Prediction Project
- GCIP: GEWEX Continental International Project
- These OGP programs are major sponsors of advancements in land surface modeling and land data assimilation in NCEP’s Environmental Modeling Center (EMC)

## The N. American Regional Reanalysis:A Coupled Atmosphere-Land System with hourly assimilation of observed precipitation

- In the forecast period between the analysis steps of the 12h pre-forecast data assimilation period, at each time step and at each point where observed precipitation is available, we compare *P**mod* to *P**obs*, then modify the model’s temperature, moisture, cloud and rain field to be more consistent with observed precipitation.
**See NARR Article in May 2004 issue of GEWEX/NEWS**

**The N. American Regional Reanalysis:****A Coupled Atmosphere-Land System with ****hourly assimilation of observed precipitation**

- Pre-forecast data assimilation period

- Free forecast period

## Noah Land Surface Model in NARR

**Noah Land Surface Model in NARR**

** **_**4 soil layers**_**: 10, 30, 60, 100 cm**
-    -- total soil moisture
-    -- liquid soil moisture
-    -- soil temperature
** **_**cold season processes**_** **(*Koren et al 1999*)
-   -- snowpack     --- snow  water equivalent (SWE)     --- snow depth (i.e. snow density)
-   -- frozen soil freeze/thaw physics
-  **- **_**surface evaporation components**_
-   -- Et: transpiration through plant canopy
-       --- Jarvis treatment of canopy resistance
-   -- Ed: direct soil evaporation
-   -- Ec: canopy evaporation
-       --- dew/frost, intercepted precipitation
-   -- Es: snow sublimation
**Vegetation phenology from NESDIS**
**  **-- based on AVHRR NDVI
-   -- fraction of green vegetation cover
-   -- monthly global 0.144-deg 5-year climatology
-   -- derived by Gutmann and Ignatov 1998)

## GREEN VEGETATION FRACTION PHENOLOGY:

**GREEN VEGETATION FRACTION PHENOLOGY:**
- via NESDIS/ORA based on NDVI from AVHRR** **

- From global, monthly 0.144 degree green vegetation fraction database (Gutman and Ignatov, 1998).
- Monthly values are  interpolated to daily values in the NARR.

## Example NARR Cold Season States:  01 Jan 92, 21Z

- Snow depth (cm)

**Shown later**:  Daily update increment of snowpack state from
- U.S. Air Force analysis must be included in land surface water budget.

## Key references for Noah LSM physics & performance

- Chen et al. (1996, JGR, GCIP Special Issue 1)
- Canopy resistance and soil hydraulics/thermodynamics
- Koren et al. (1999, JGR, GCIP Special Issue 2)
- Snowpack and frozen soil physics
- Ek et al. (2003, JGR, GCIP Special Issue 3)
- Improvements to subsurface heat fluxes under snow and vegetation
- Berbery et al. (2003, JGR, GCIP Special Issue 3)
- Ops Eta model Mississippi land surface water and energy budget
- Mitchell et al. (2004, JGR)
- Evaluation in uncoupled N. American LDAS (NLDAS)
- Also see various PILPS and GSWP papers
- Uncoupled Noah LSM performance

## Land Surface Subset of NARR output

**NARR Land-surface subset**:  – **720 Gb / 25-year**
- 75 Mb/day tar file of Land A, B, C files
- versus 450 Mb/day for primary output
- via NCEP _NOMADS_ server (free anonymous access)
- Land A files: surface forcing (155 Gb for 25-years)  -- can drive offline LSMs
- Land B files: includes prognostic land states (soil moisture/temp., snow)
- Land C files: includes surface water and energy fluxes
- from which to calculate land surface water and energy budgets
- 3-hour _accumulated_ water fluxes (mm)
- 3-hour _averaged_ energy fluxes (watts/m**2)
**Also need Merged B file to**:
- calculate daily snow update increment in surface water budget
- obtain _instantaneous_ surface energy fluxes (vs. 3-hr mean in Land C file)
**Fixed field file **(18 fields, veg and soil type, land mask, etc)
- Soil and veg parameter fields (soil porosity, wilting point, min stomatal resistance)

## 18 NARR Fixed Fields

**Examples:**
- Terrain height
- Land mask
- Latitude
- Longitude
- Soil type (Zobler classes: 7 classes)
- Vegetation type (SiB classes: 12)
- Number of soil layers in root zone
- Maximum albedo over deep snow
- Wilting point
- Soil porosity
- Minimal stomatal resistance

## Example Fixed Fields

**Example Fixed Fields**

- Vegetation Class: 12 Sib classes

- Maximum albedo over deep snow

## Hourly output at over 1300 sites:(especially convenient in 72-hour free forecasts)

- Atmospheric sounding
- Near surface forcing
- Land surface fluxes
-   -- latent & sensible heat flux

- Examples shown here:

## Drought of 1988 vs. Flood of 1993

## Difference of 1993 minus 1988

**Difference of 1993 minus 1988**
**June + July Mean Precipitation**

## NARR: 

**Soil moisture availability (percent of saturation)**
**Top 1-meter of soil column**

**1993**

**1988**

_**Average during 16-31 July**_** ****(at 21 GMT)**

## NARR: 

**Atmospheric Boundary Layer Depth (m)**

**1993**

**1988**

_**Average during 16-31 July**_** ****(at 21 GMT)**

## NARR Annual Mean Fields (1998-2002)(to compare with ops EDAS in Berbery et al., 2003, JGR)

## 2004 mean July 18Z-21Z average latent heat flux(sign convention: negative values are upward flux from surface, i.e. surface energy sink)

**Ops 12-km EDAS**

**Global Reanalysis 1**

**32-k Regional Reanalysis**

**NLDAS**

## Observed Latent Heat Flux (LH): 3 stations( Tilden Meyers: NOAA/ARL, eddy correlation stations )(Brotzge and Crawford, JHM, 2003: such stations give 20% low bias summer LH)

## NARR Summer Latent Heat Flux in eastern half of CONUS appears to have modest high bias

- _Hypothesis as to why_: Ops EDAS legacy in NARR
- Ops EDAS has low precipitation forcing bias
- Ops EDAS assimilates radar dominated precip estimates
- Radar-dominated precip estimates have low bias over summer CONUS
- Above ops EDAS precip forcing bias tends to yield dry soil moisture bias in Ops EDAS
- Ops Eta model canopy resistance was tuned to lower values to maintain reasonable evaporation rates given low soil moisture bias
- Ops Eta model canopy resistance was not re-tuned to higher values for Regional Reanalysis, which has less low  bias in its precipitation forcing, which is anchored to a daily gauge analysis

## Example of low precipitation bias in ops EDAS:Observed (Higgins/Shi CPC) minus ops EDASprecipitation total (mm) for August 2003

## High bias in NARR eastern-CONUS warm season evaporation overly depletes soil moisture over Illinois

- Obs:
- solid black
- NLDAS:
- dashed green
- NARR:
- dashed black

- Courtesy:
- Y. Fan &
- H. Van den Dool

## NARR Monthly Water Budget: 1998-2002 Mississippi Basin (compare with ops EDAS by Berbery et al. (2003, JGR) 

- (ops Eta min)

## NARR Monthly Water Budget: 1998-2002 Mississippi Basin (compare with ops EDAS by Berbery et al. (2003, JGR) 

- Black: no snow update
- Green: with snow update

## Must include daily NARR snowpack update in the NARR land surface water budgetAt 00Z each day, NARR snowpack depth is updated from external daily Air Force snowdepth analysis

-      21Z
-    NARR
- Anal –Ges
- SWE (mm)
-    is zero

-      03Z
-    NARR
- Anal –Ges
- SWE (mm)
-     is zero

-      **00Z**
**   ****NARR**
**Anal –Ges**
**SWE (mm)**
**is **_**nonzero**_

-     00Z
-   NARR
-    Anal
- SWE (mm)

## Snowpack physics in Noah LSM Version 2.6  in NARR has systematic tendency to melt snowpack too early

- This systematic behavior was solved in Noah Versions 2.7 and higher, _but not in time to make it into the production NARR_ (despite concerted attention to this problem)
- Hence daily snowpack increment will tend to be positive (increase snowpack SWE)
- Acts as a source of water mass to land water budget
- Above two factors will likely yield a high bias in season-total snowmelt

## NARR Verification: Near Surface  Meteorology

- _Compared to Global Reanalysis_:
- NARR has smaller 2-m temperature bias and rms error in both winter and summer
- NARR has less diurnal cycle error

## July 1998

_**January**_** 1998**

- 2-m  Temperature 1st Guess

- RMS

- Bias

- RMS

- Bias