Multiple Sensor Precipitation Estimation over Complex Terrain

AGENDAI. Paperwork A. Committee member signatures B. Advisory conference requirements

II. Bulk of meetingA. Importance of rainfall monitoring in the WestB. Goals of M.S. researchC. Problems with precipitation estimates in complex terrain D. Multi-sensor approachE. Methodology

1. Case study (events)2. G vs. R comparisons3. Application of Methods #1 and #2

III. Timeline

IV. Discussion

Multiple Sensor Precipitation Estimation over Complex Terrain

Importance of Rainfall Monitoring in the West

Water management in the West is becoming increasingly important as heavily populated cities demand more water.

Today, companies involved in dam management, such as the Salt River Project (SRP), rely on limited rain gauge measurements to predict streamflow from a given watershed.

Peak flows from a basin often result from a combination of snowmelt and rainfall. Clearly, there is a need to accurately estimate basin-wide precipitation in the West to predict the resultant streamflow.

Since hydrologic models of the near future will rely on WSR-88D measurements, we need to be able to IMPROVE our monitoring capabilities of both snow and rainfall in the West.

Multiple Sensor Precipitation Estimation over Complex Terrain

Goals of M.S. Research

Discover possible error sources within the PPS in current precipitation estimates made by the WSR-88D.

IMPROVE basin-wide precipitation measurements on a realistic time scale by:

A) Utilizing rainfall measurements from radar, rain gauges, and satellite.

B) Blending radar data for regions which fall underneath the umbrellas of 2 or more radars.

C) Explore ways to use satellite IR imagery to apply a model of reflectivity structure to blocked regions.

Multiple Sensor Precipitation Estimation over Complex Terrain

Study Region showing AZ terrain

Multiple Sensor Precipitation Estimation over Complex Terrain

Problems with Radar Precipitation Estimates in Complex Terrain

1. Sampling Issues

2. PPS Issues Z-R relationship Grid transformation Gauge correction

Multiple Sensor Precipitation Estimation over Complex Terrain

Problems with Rain Gauge Measurements in Complex Terrain

Spatial coverage of rain gauge network is often limited and fails to capture large precipitation gradients which are common in areas of high relief.

Gauges provide data on an hourly basis, but they report at varying times after the hour.

Most sensors often are not equipped to handle mixed-phase precipitation events (i.e. tipping bucket gauges).

Splash-out, underestimating the catch, wind effects, debris blockages, just to name a few.

Multiple Sensor Precipitation Estimation over Complex Terrain

Problems with Satellite Precipitation Estimates

Derived rainfall accumulations are too coarse for basin-wide precipitation estimations.

Estimates are subject to the “cirrus problem”. The vertical depth and cloud top temperature of certain cloud features are not always well-correlated with rainfall reaching the surface.

Some precipitating systems produce rain via warm rain processes.

In the West, the lower troposphere can be quite dry and result in significant sub-cloud evaporation.

Multiple Sensor Precipitation Estimation over Complex Terrain

Dual Radar “Multi-Sensor” Approach

Many precipitating systems in the West during the winter can be of a stratiform type. Perhaps it is best to sample these clouds at the lowest available tilt from either radar (“optimal height sampling”).

Many regions which are blocked from one radar’s perspective are quite visible from another radar.

By utilizing data from 2 radars, we can obtain precipitation estimates which were

sampled at differing heights.

The dual radar approach allows radar data to be blended from adjacent radars.

Multiple Sensor Precipitation Estimation over Complex Terrain

Single Radar and Satellite “Multi-Sensor” Approach

Due to the complexity of the terrain and associated radar blockages in AZ, precipitation estimates for many regions are sampled at a height near or above the tropopause!!!

This problem worsens in the case of shallow, precipitating clouds which are not uncommon in the winter.

The multi-sensor approach attempts to improve precipitation estimates in regions which are inadequately sampled by the WSR-88D network.

In essence, a model will be applied which correlates reflectivity structures (profiles) to cloud top temperatures.

This relationship can then interpolated to areas which are shadowed by mountains.

Multiple Sensor Precipitation Estimation over Complex Terrain

Single Radar and Satellite “Multi-Sensor” Approach

Obtain the vertical reflectivity structure of a precipitating system in a region which is well-sampled by the WSR-88D (every 15 minutes).

Similarly, determine the associated average cloud top temperature of the system by GOES 9 IR imagery.

Next, obtain the average cloud top temperature of cloud features in the “problem area”.

With this, derive a vertical reflectivity profile for this region. This model will need to be adjusted in the case of contrasting average cloud top temperatures.

Utilizing “optimal height sampling”, retrieve the reflectivity value in the problem area and convert to a rainfall rate (standard Z-R).

Update the REF profile-IR relationship every 15 mins.

Multiple Sensor Precipitation Estimation over Complex Terrain

Multi-Sensor Approach (Single Radar and Satellite)

Height

Reflectivity

Sat IR Brightness Temp

Sat IR Brightness Temp

Adjusted Reflectivity

Height

Precipitation Estimation in Complex Terrain - A Multi-Sensor Approach

Height

Reflectivity

Sat IR Brightness Temp

Sat IR Brightness Temp

Adjusted Reflectivity

Height

Precipitation Estimation in Complex Terrain - A Multi-Sensor Approach

Multiple Sensor Precipitation Estimation over Complex Terrain

Methodology

Collect archive level II radar data, GOES 9 IR imagery and rain gauge accumulations for 2-3 cases of widespread precipitation affecting a large area in central AZ.

For each event, objectively determine the performance of both radars using rain gauge data as ground truth.

Next, identify the “problem areas”, and attempt to ascertain why the radar-derived precipitation estimates are in error.

Apply the multi-sensor methods and determine the magnitude of the improvements made upon the basin-wide precipitation estimates.

Multiple Sensor Precipitation Estimation over Complex Terrain

Methodology - Case Study (Events)

How were cases chosen?

Major watershed event in the Salt and Verde River Basins - determined by average precipitation amounts.

Thus far, we have identified 9 cases which fit the above criteria.

These cases are mixed-phase, widespread precipitation events which occur over 1-2 days.

Will narrow these cases down to 2-3 case events for detailed study.

Multiple Sensor Precipitation Estimation over Complex Terrain

Methodology - G vs. R comparisons

Using GIS software, I am now able to objectively analyze the radar-derived precipitation data versus the rain gauge accumulations.

Next, I will segregate the G-R statistics by the elevation of the rain gauge and by the height from which the reflectivity value was obtained (sampling height).

Where are the problem areas and why are they there?

Is the error a function of gauge location w.r.t. the radar?

Multiple Sensor Precipitation Estimation over Complex Terrain

Methodology - Application of Methods #1 and #2

Multiple Sensor Precipitation Estimation over Complex Terrain

Timeline April 8 - Have all data collected and formatted

April 17 - Identify all problem areas based on G-R comparisons

May 1 - Begin implementing methods to improve basin-wide precipitation estimates

May 15 - Determine how well the methods worked

June 1 - Begin writing thesis

August 3 - Goal: Draft of thesis

August 28 - Finalize revisions

September 8 - Give thesis seminar

September 10 - Defend thesis