# Presentation: 200376

## EFFECT OF ANTHROPOGENIC MOISTURE ON SHORT RANGE FORECASTS FOR THE LOS ANGELES BASIN Leslie BelsmaRich ColemanJames Drake

- © 2008 The Aerospace Corporation

**Notes:**
The purpose of the study is to implement data assimilation of Cloud Liquid Water (CLW) Environmental Data Records (EDRs) derived from measurements made with Advanced Microwave Scanning Radiometer (AMSR-E) sensor on the NASA EOS Aqua satellite.
Aerospace, with the support of an NPOESS IGS, extended some variational assimilation software used by the weather community to give it the ability to assimilate AMSR-E total CLW EDRs.  
The assimilation of these cloud data results in a better estimate of the synoptic three-dimensional distribution of the cloud liquid mixing ratio. 
The initial result of assimilating total cloud liquid is a field called the increment of cloud liquid, which is the change to a first-guess three-dimensional distribution of the cloud liquid mixing ratio. 
The algorithm for converting the AMSR-E Sensor Data Records (SDRs) to total cloud liquid EDRs is valid over large lakes and oceans, not land, so the assimilation affects forecasts driven by systems moving in over large bodies of water.

## Outline

- Background
- Approach
- Moisture Sources
- Moisture Data Base Development
- Model Modification
- Results

## Model optimization studies suggested that missing physical processes in NWP models limit the benefits in improved forecast accuracy from ingesting more satellite data.
Runs we have done with both MM5 and WRF have shown peak summer daytime temperatures to be overestimated. 
Our hypothesis is that this is because anthropogenic “precipitation” from irrigation and domestic water use has not been included – or has been underrepresented – in the models.
Our objective is to incorporate anthropogenic moisture sources in weather simulations/forecasts and to compare them with forecasts made without these sources to see how much difference they make.

- Runs we have done with both MM5 and WRF have shown peak summer daytime temperatures to be overestimated.
- Our hypothesis is that this is because anthropogenic “precipitation” from irrigation and domestic water use has not been included – or has been underrepresented – in the models.
- Our objective is to incorporate anthropogenic moisture sources in weather simulations/forecasts and to compare them with forecasts made without these sources to see how much difference they make.

**Background**

## Approach

- Determine order of magnitude amount of moisture released by human activity
- Compare the amount of anthropogenic moisture to moisture from natural sources
- Determine the spatial and temporal (diurnal, seasonal) variation of anthropogenic moisture release
- Develop models of equivalent precipitation for each major anthropogenic moisture source
- Develop a static data base of LA basin anthropogenic moisture sources in a format compatible with WRF
- Modify WRF to ingest the anthropogenic moisture
- Compare high resolution (5km) forecasts with and without added moisture

## Identification of moisture sources

- Identified types of human water consumption and obtained estimates of total use
- Determined normal precipitation by counties of interest for comparison
- Subsequent chart shows natural and human-provided amounts for counties in our inner, 5-km resolution, domain
- Power plants not a part of this initial study but use very large amounts of water for cooling

## Water provision (Mgal / day)

- Note the logarithmic scale

## Slide 7

## Data Sources

- Precipitation data
- Western Regional Climate Center - Western US Historical Summaries (Individual Stations)
- Water usage
- USGS Circular 1268, “Estimated Use of Water in the United States County-Level Data for 2000”
- USGS “Guidelines for Water Use Estimates”
- Power plant locations and output
- 2005 DOE Electric Information Administration (EIA) Form EIA-860, "Annual Electric Generator Report”

## Temporal variation of anthropogenic water

- The USGS data are only annual averages.
- Water use at least throughout the year is needed.
- Consideration of approaches led to data gathered and analyzed by the state of California Department of Water Resources (DWR).
- DWR, in cooperation with UC, Davis, manages a network of 177 automated weather stations under the California Irrigation Management Information System (CIMIS) program.
- CIMIS automated stations collect data at 60 second intervals, and average it for hourly and daily periods.
- Data are ingested in an evapotranspiration model.
- DWR has compiled monthly and annual evapotranspiration amounts for a set of 18 zones that cover California

## Evapotranspiration Zones of California

- (Reprinted courtesy of CIMIS.)

## Temporal Variation of Evapotranspiration

- We allocated the annual irrigation and domestic use amounts according to the monthly profiles of evapotranspiration statistics
- Examples from two counties: Orange County, on the coast

## Temporal Variation of Evapotranspiration

- Imperial, an inland county

## Spatial Distribution of Anthropogenic Moisture 

- We distributed the moisture spatially using satellite-derived land-use data.
- Decided to use the same land-use data as WRF itself, which has 30’ resolution and 24 categories.
- Four categories of irrigated croplands and one of urban land use
- Area-weighted average monthly fraction of evapotranspiration developed separately for urban and irrigated lands.
- The fraction was used to divide the total anthropogenic moisture available and distribute it in space.

## Example of Monthly Anthropogenic Moisture - June

## Model, Configuration, and Domain

- WRF (ARW) Version 2.2
- ETA-TKE PBL scheme
- NOAH Land Surface Model (4-soil layers)
- 5-km grid on inner domain, 15-km grid on outer domain, initialized with 30’ (0.9 km) terrain data
- Inner and outer domains interact
- 37 vertical levels; model top is 100 hPa
- Rapid Radiative Transfer Model (RRTM) with recalculation every 30 min
- Grell’s cumulus parameterization only in the outer domain
- Initial- and lateral boundary-conditions from the NCEP North American Model at 40 km
- Urban canopy submodel on for consistency with setup for control runs

## Results

- The temperature (T2) and mixing ratio (Q2) at 2 m AGL are model output variables assessed.
- Determined a field of temperature differences (ΔT2) at the 12th forecast hour, averaged over 4 WRF runs (four days in July 2007)
- Generated statistics of ΔT2 and ΔQ2 at 12th forecast hour for each of the four days: mean, median, and histogram
-    Δ X      represents X_anthropogenic – X_control
- < Δ X >   represents the mean of Δ X over all 2m grid points in the domain
- med X     represents median value.

## 4-day Mean of ΔT2 at 12th Forecast Hour

- Overall throughout the nested inner domain, the 12 hour forecast T2 temperatures for the WRF run with anthropogenic sources are cooler than T2 for the control run without the additional moisture source

## Combined Statistics of  ΔT2 (K) and ΔQ2 (g/kg)

| Day of July | 1 | 5 | 6 | 7 |
| --- | --- | --- | --- | --- |
| < ΔT2 > | -0.01599 | -0.0230 | -0.0210 | -0.0020 |
| med ΔT2  | -.00586 | -0.0033 | -0.01129 | -0.0033 |
| < ΔQ2 >
 | +0.0965
 | + 0.0378 | +0.0848 | +0.0548 |
| med ΔQ2  | +0.0238 | +.00358 | +.00289 | +0.01579 |

- med X is the median of X

## Remarks on the Table of Statistics

- The differences are all in the right direction for both means and medians.
- Nevertheless the differences in the global statistics are small, although, as the image showed, point-to-point differences can be significant.
- This is only at the 12th hour of the forecast, which started at 1700 local daylight standard time, so evapotranspiration is probably at a low point during this part of the simulation since temperatures are cool or cooling.

## Histogram of Combined ΔT2 for the 4-days 

- The primary mode is negative.
- We know from the statistics table that the average for the four days of ΔT2** **is negative, because they are each negative separately. We couldn’t get that simply from this figure because of the logarithmic transformation.
- It appears that the distribution is negatively skewed.

**Notes:**
We did a logarithmic transformation on the histogram to emphasize more of the tails of the distribution of delta T2. Some bins of the histogram were empty so we added 1 to each bin before taking the log to avoid any singularities. 

## Plans

- We’ve already begun running out  to the 48th forecast hour, our intended point of comparison.
- Intend to do this for July and August of 2007.
- Verify the runs of the modified and standard WRF model directly against available observations, particularly for T2.
- Incorporate a submodel to represent moisture from power plants and qualitatively assess the impact.

## Questions?