DoD Poster Howell 2010

The Relationship between Rainfall Rate and Total Precipitable WaterThe Relationship between Rainfall Rate and Total Precipitable WaterKelly HowellKelly Howell11, Thomas H. Vonder Haar, Thomas H. Vonder Haar1,21,2, and Stanley Q. Kidder, and Stanley Q. Kidder22

Howell@cira.colostate.edu

1-Colorado State University Department of Atmospheric Science, Fort Collins, CO 80523-13712-DoD Center for Geosciences/Atmospheric Research; Cooperative Institute for Research in the Atmosphere, Fort Collins, CO 80523-

1375

No Annual Cycle

With Annual Cycle

Introduction Mean TPW given Rain Rate

References

Data/Products

Estimate moderate to heavy rain in areas lacking adequate forecasting capabilityAnticipating heavy rain is useful for:

Flood predictionHeavy rain estimates for flood warnings in DoD Tactical Decision AidsTrafficability

Applications

Joyce, R.J., J.E. Janowiak, P.A. Arkin, and P. Xie, 2004: CMORPH: A Method that Produces Global Precipitation Estimates from Passive Microwave and Infrared Data at High Spatial and Temporal Resolution. J. Hydrometeor., 5,487-503.Kidder, S.Q. and A.S. Jones, 2007: A Blended Satellite Total Precipitable Water Product for Operational Forecasting. J. Atmos.Oceanic Technol., 24, 74-81.

AcknowledgementsThis research is supported by the DoD Center for

Geosciences/Atmospheric Research at Colorado State University under Cooperative Agreement W911NF-06-2-

0015 with the Army Research Laboratory.

RR vs TPW over an area off the coast of Florida. There is not

a 1:1 relationship between TPW and

RR. However, a relationship does exist; higher rain

rates are more likely for certain TPW

values.

Study time: Feb. 2006 – Dec. 2008; 3924 casesStudy area: 60o N to 60o S6 hour time resolution0.25o spatial resolutionTotal Precipitable Water from CIRA bTPW product

Uses passive microwave sensorsAMSU, SSM/I instruments on 6 satellites

Rain Rate from CMORPH productUses passive microwave sensorsAMSU, SSM/I, TMI instruments on 7 satellites

Comparing the peak of the TPW distribution and the slope of the RR distribution

for each grid box and comparing these quantities

globally indicates a latitude-dependent

relationship.

RR > 2.0 mm/hr

0.1< RR <0.5 mm/hr

For higher rain rate values, the distribution of TPW is skewed toward higher values. For instance, for RR > 2.0 mm/hr, there are relatively few cases of TPW < 12.5 mm, whereas for 0.1< RR <0.5 mm/hr, those lower

TPW values are more prevalent. These plots represent 35 months of data. The red dots indicate areas that have been more intensely studied.

Atmospheric water vapor is a necessary condition for precipitation, an important variable in the global water budget. This project intends to better understand the relationship between total precipitable water (TPW) and rainfall rate (RR) in order to facilitate rainfall prediction in the absence of direct measurements. Understanding these two quantities is potentially useful for several purposes, including predicting the probability of rain, analyzing the performance of models, and comparing satellite retrieval algorithms. Although this study is mainly over the ocean, the same concepts may be used in a similar study when a valid dataset for TPW over land becomes available.

The probability of rain (i.e., RR>0.1

mm/hr) off the coast of Florida. Rain is unlikely

for TPW < 40 mm. Similar plots were

made for the other areas

indicated on the global maps

above, each with different features,

indicative of spatial variations.

Distributions of TPW and RR

Future WorkAnalyze and compare the RR slope vs TPW peak plots to determine if significant spatial and/or temporal variations existBreak down the data in a latitude zone into its wet and dry regions to determine if a climate-dependence existsFocus on the properties of ‘important’ areas, such as Indonesia, India, Korea, and Japan

Distribution of TPW off the coast of FloridaDistribution of RR off the coast of Florida

Information about the probability of rainfall is useful for forecasters.