# Presentation: 502354

## The Urban Atmosphere Research Program 

**Focus –** To take our forecasts and analyses to where people live and work.
**Issues --**
-      ***For forecasting:***
- 	    	**Dispersion**		Floods
- 		Fronts			Heat waves
- 		Cold spells		Icing
- 	    	**Fires**			**Air Quality**
-      ***For data acquisition and analysis:***
- 	    	Climate			**Ecosystem loading**
**Data requirements are different for the various issues.  A central requirement is for an improved capability to predict the surface boundary layer accurately, and for data to support it.**

## Street canyons present great difficulty.  What should we predict --  

- Street canyons present great difficulty.  What should we predict --
- [C]  or  P([C] > [C0])?
- “Skimming flow” is driven by the meteorology aloft.  The in-canopy environment  is  controlled by the configuration of streets and buildings, traffic patterns, etc.

- Washington, DC, and New York illustrate two extremes

-  Washington has broad streets and low buildings.
-  New York has deep and sometimes narrow street canyons.

- Physical models and computational fluid dynamics models are being used, in
- parallel.

## U

- Avg. bldg height

- Rooftop
- Towers (1c)

- T

- T

- T   Surface Towers (1a, 1b)

- Tier 1  -- Surface towers
- 1a  Conventional
-    meteorological systems
- 1b  Enhanced systems
- 1c  Specialized systems
- Tier 2 – Remote sensing
-     (RADAR, etc.)

- RADAR
- LIDAR (2)

- The proposed UrbaNet configuration

- T

- “UrbaNet” is DCNet in other areas (Tier 1c).

- A vision for measurements --

**http://dcnet.atdd.noaa.gov/**

## Some challenges for use of urban datasets for meteorological models

- (Courtesy Bruce Hicks)
**Hard reality check #1 **
- Meteorological models are constructed from the understanding of processes, each of which is represented as an average behavior.
- If the models are essentially built from understanding of averages, they should not be expected to apply except on the average.
- We are no longer interested solely in the average.  We need to address specific instances.  Hence, the requirement for more data is extreme.

## Hard reality check #2 – 

- Meteorological models are becoming increasingly refined.  However in daytime the convective process in the boundary layer is largely stochastic and hence deterministic models should not be expected to reproduce behaviors on the scale of convective updrafts, except on the average.
- Hence, conventional mesoscale models should not agree well with observations taken over scales that are less than several kilometers, unless these observations are ensemble-averaged.
**Hard reality check #3 – **
- In theory and in practice, micrometeorological descriptions of the surface apply above about ten times the roughness length (several m) above the zero plane (~ 80% of the average structure height).
- Typically – RADAR cannot look low enough, and towers are not high enough.

## NOAA Modeling Applications

- Incorporate urban scale parameterizations in mesoscale models
- Focused evaluation of  NWS non-hydrostatic model forecasts (~4 km) with urban datasets
- WRF-NMM High Resolution Window nests (East/West/AK/HI/PR)
- NEMS NMM-B 4 km CONUS Nest in NAM by 2010
- Real Time Mesoscale Analysis (RTMA – 2.5  5 km)
-  Air Quality Forecasts
- CMAQ: Ozone, fine particulate matter
- HYSPLIT/GOCART: smoke, dust
- Regional Reanalysis (1979-Present, North America 32 km)
- Provide high resolution  meteorological uncertainties using ensemble techniques
- SREF : 32 km, 4x/day 84 hour forecasts
- GENS : 1 degree, 4x/day, 16 forecasts
- HREF experimental 12 km forecasts for Eastern U.S. & Beijing Olympics
- Explore mesoscale data assimilation techniques with urban datasets
- GSI 3-D VAR

## ESMF Component Framework:National Environmental Modeling System (NEMS – NMMB, GFS, FIM, ARW...)

**Atmosphere**

**ATM**
**Dynamics**

**ATM**
**Physics**

**Mark Iredell (NCEP)**

**ATM**
**Chemistry**

**Notes:**
This diagram illustrated the overall framework (from Mark Iredell).  The developments of gridded GFS and unified post are currently in progress and are expected to be available mid-late 2007.    Once gridded GFS is available, it will be coupled by GOCART grid component via ESMF coupler.

The coupling between GFS and GOCART will be done via the ESMF coupler rather than include GOCART routine inside GFS.  We discussed the option for ozone chemistry with Steve some time ago (~ Nov 2006).  I recall that we decide (or prefer) to have ozone chemistry coupled with GFS via the coupler as well.   If so, the ATM ozone chemistry will be a stand-alone grid component and theb coupled with GFS via the coupler (similar to GOCART grid component).

## North American ModelExpanded Domain 18% larger

***North American Model (NAM) WRF run 4x/day at 12 km to 84 hours***

**Notes:**
Covers Alaska and Hawaii for possible dispersion applications.

## 	HiRes Window Fixed-Domain 4 km Runs 

- _FOUR_ routine runs made at the same time every day
- 00Z : ECentral & Hawaii
- 06Z : Alaska  & Puerto Rico
- 12Z : ECentral & Hawaii
- 18Z : WCentral & Puerto Rico
- Everyone gets daily high resolution runs _***if & only if***_ hurricane runs are _not_ needed
- implemented in Q4FY07

- _[http://](http://www.emc.ncep.noaa.gov/mmb/mmbpll/nestpage/)__[www.emc.ncep.noaa.gov/mmb/mmbpll/nestpage](http://www.emc.ncep.noaa.gov/mmb/mmbpll/nestpage/)__[/](http://www.emc.ncep.noaa.gov/mmb/mmbpll/nestpage/)_

## HRW Homeland Security/Fire Wx Nests (26)

**HRW Homeland Security/Fire Wx Nests (26)**
**WRF-NMM Model @ 2.66 km**

## Real Time Mesoscale Analysis (RTMA)

- Temperature & dew point at 2 m & wind at 10 m
- RUC forecast/analysis (13 km) is downscaled to 5 km NDFD grid
- Downscaled RUC used as first-guess in NCEP’s 2DVar analysis of ALL surface observations
- Estimate of analysis error/uncertainty
- Precipitation – NCEP Stage II analysis
- Sky cover – NESDIS GOES sounder effective cloud amount
*Currrently adding a Planetary Boundary Layer Analysis into RTMA: Radiosondes, ACARS, MPLNET, GPS-RO*

## Error Correlations for Valley Ob (SLC) Location Plotted Over Utah Topography

- _Anisotropic_ Correlation:
- obs' influence restricted to
- areas of similar elevation

- _Isotropic_ Correlation:
- obs' influence extends up mountain slope

## Example of revision in RTMA-RUC downscaling based on 2006 review

*Example of revision in RTMA-RUC downscaling based on 2006 review*
- Revised code to generate downscaled
- NDFD 1st guess constrains the upward
- extrapolation that previously led to too
- warm 2-m temps over high terrain
- during early morning inversions

- Diffs

## All Mesonet Obs = 78283

## of which AWS Obs = 35565

## TX 2 m Temperature Analysis

## Questions for this Group

- What data are needed for:
- Meteorological evaluation and data assimilation
- To drive and evaluate dispersion models
- Spatial and temporal resolution ?
- Winds, temperature, moisture
-  Mixing depth
- Sigma u,v, θ; TKE
- Tracer releases
- Vertical Profiles vs surface observations