The Hydrologic Cycle: How Do River Forecast Centers Measure the Parts?

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1 The Hydrologic Cycle: How Do River Forecast Centers Measure the Parts? Greg Story Meteorologist National Weather Service Fort Worth, TX

2 Overview n Introduction What is the mission of an RFC? n The Hydrologic Cycle what can be measured directly, and what is not? n Purpose of the Hydrometeorologists at an RFC (We will use WGRFC as the primary example) n Bottom line we need more CoCoRaHS observers!

3 My Background n Graduated from Northern Illinois University in 1976 (NIU is 70 miles west of Chicago) n Was a broadcast meteorologist for 14 years n Joined the NWS in early 1990 as a met intern in Shreveport, LA. n I ve been with the NWS WGRFC in Fort Worth, Texas since November, I am a hydrometeorologist. That means I focus on the aspects of meteorology that influence hydrology (flooding, water supply and drought).

4 What is my main responsibility? n As a hydrometeorologist, it is my responsibility to determine how much rain fell in the past, how much rain is falling now, and how much rain will fall in the next 7 days. I do this for most of Texas, most of New Mexico, southern Colorado, and northern Mexico. n It s not easy determining how much rain is falling right now!

5 WFOs and RFCs 122 WFOs in the U.S. and territories 13 RFCs Nationwide

6 The West Gulf RFC Area Snowpack Water supply 402,000 mi 2 total area 87,000 mi 2 in MX (Rio Grande tributaries) 320 forecast points, 15 major river systems Rocky terrain Flash flood threats Rapid river responses Prolonged river flooding Complex reservoir operations Tropical threats Storm surges Coastal flooding

7 Main West Gulf RFC Duty: River Flood Forecasts n To accomplish this duty, we have 2 work groups: u Hydrometeorological Operations Past, present and future precipitation data and forecasts t HAS = Hydrometeorological Analysis and Support t 3 HAS Forecasters at WGRFC all meteorologists u Hydrologic Operations River data and forecasts t 11 Hydrologists at WGRFC some are professional engineers n What do you do when it isn t flooding? Research and development augments and compliments our operations

8 Why is flood forecasting so difficult? n Accuracy of measuring the hydrologic cycle.

9 Why is flood forecasting so difficult? n Amount of water vapor can be measured. Water vapor comes from evaporation (oceans, lakes and the ground) or transpiration (trees and vegetation).

10 WV is Measured using Weather Sensors On the Ground (dew point)

11 In the Upper Atmosphere (Radiosondes)

12 And by satellites. Transport of water vapor is important.

13 Why is flood forecasting so difficult? The Problem begins on the precipitation side

14 Knowing the precise amounts of precipitation reaching the ground is essential in determining runoff and, in turn, river flooding

15 How much precipitation is falling? What sensors do we use? n Most of our determination of how much precipitation is falling is now radar-based (the timing and location of the rain from radar is outstanding) n Hourly rain gauges are used for biasing radar estimates n Satellite-derived precipitation estimates are used where radar data and gauges don t exist (northern Mexico)

16 We get estimates from Radar

17 but these estimates can be in considerable error! n So why do we use radar-based estimates as the baseline products for our precipitation analyses? There is nothing better than radar. This shows hurricane Ike.

18 Other precipitation sensors we use have problems, too! 1. Automated Rain Gauges n Wind n Precip rates, tipping buckets 2. Satellite Precipitation Estimates n Based mainly on IR temperatures n Spatial resolution 3. Radar QPE n Z/R relationships and Drop Size Diameter Assumptions n Only recently started using dual polarization

19 Problems with Rain Gauges (especially Automated ones) n Wind n Tipping bucket losses n Siting (blockage from buildings, trees, plants, decks, bridges) n Frozen Precip n Electronic signal malfunctions n Mechanical problems n Bird Nests n Timing and Coding Issues The siting of gages is critical.

20 Rain Gauge Data Wind: n At 10 mph, loss ~10% n At 15 mph, loss ~20% n 39 mph, 50% loss n Expect a 20% error during thunderstorms (1 inch error for 5 inch storm!) Tipping bucket: n Can t keep pace with intense rainfall n Lose precip while bucket tips (1/2 sec) n Can t be calibrated for 0.01 precision and for high rates n 6 in/hr rate with zero error will require a little more the 0.01 in per tip.

21 Rain Gauge Data n There is no substitute for manually read rain gauge data from an analog gauge in real-time! n Making a report stating that I ve got a rain rate of 2 inches per hour does NOT help the NWS. Radar already tells us it is raining hard in a location. But radar does not tell us an exact amount.

22 Satellite Precipitation Estimates IR Satellite Image SPE from Hydroestimator

23 Satellite Precipitation Estimate Weaknesses CB Top Tb=198 K Cirrus, Tb=208 K n Overestimate precip from cirrus (almost as cold as the top) n Underestimate precip from some shallow cloud systems Nimbostratus Tb=243 K

24 Radar Precip Estimates n Brief explanation of how radar estimates rainfall Radar sends out beam of electromagnetic energy. A small amount of this energy returns to the radar (measured in decibels or dbz) from raindrops (and other objects) and is called reflectivity Z A relationship assigns a rainfall rate R to the reflectivity Z (Z/R).

25 Problems with Radar Precipitation Estimates n Loss of low-level information (beam blockage due to terrain), distance (due to range degradation), and/or anomalous propagation ( AP ) n Radar assumes all reflected returns are liquid. But some is hail, sleet and melting snow (hail contamination and bright banding) n Inappropriate Z-R relationship for the drop size distribution n Radar out of calibration n Dual polarization of the WSR-88D improves estimates in many but not all events

26 Radar QPE n Drop Size Distribution n More sensitivity to drop diameter than drop density Same Reflectivity (R=0.09 ), Vastly Different Rain rates

27 Now that we have seen that there are deficiencies with all precipitation sensors, how do National Weather Service Hydrometeorologists (HAS Forecasters) Improve these Estimates? The HAS Forecasters improve estimates by closely monitoring and quality controlling rain gauges, then use these gauges to bias the radar precipitation estimates.

28 The WGRFC uses a special program to bring all precipitation sources together. It is called the Multisensor Precipitation Estimator (MPE) software The main objective of MPE is to reduce both areal-mean and local bias errors in radarderived rainfall by using radar, rain gauges and satellite so the final best estimate rainfall product is better than any single sensor.

29 Rainfall Estimation at WGRFC n We run MPE every hour. We bias the radar estimates using hourly rain gauges. To do this, we first quality control all the rain gauges. Then the radar biases are recalculated and checked. Then we remove all the AP and edit in the satellite precipitation estimates. We save that hours estimates. This becomes our Best Estimate field. n But remember these are still estimates. How do we know if they are close?

30 We conduct MPE Post Analysis We do some Monday morning quarterbacking. After 7 AM the following day we use a program called XNAV to accumulate 24 hours worth of MPE best estimate fields. This 24 hour total was from 19 October 2006

31 MPE Post Analysis We zoom in on an area of interest and display the 24-hour CoCoRaHS rain gauge data (and readings from other sources). We consider the 24-hour analog (or manually read) rain gauge data to be our best.

32 We can click on any of the rain gauges to show the precip data site. It tells us what the rain gauge value is, what the MPE value is at the location of the gauge, and the MPE value where we clicked our cursor.

33 If the WGRFC finds several gauges which are higher or lower than the original MPE estimates, we go back and adjust the radarbased estimates!

34 Example of a CoCoRaHS observation helping us to get it right The CoCoRaHS observer TX-EL-13, Maypearl 0.6 WSW, gave us a rainfall reading of 4.51 inches. Our initial MPE estimate for that location was 2.60 inches, or about ½ the amount that fell!

35 MPE 24-Hour Estimates after adjusting for the CoCoRaHS observations We went back to the hours it rained in this location and increased the MPE estimates. This allowed us to match the CoCoRaHS amount in real-time.

36 Text Product Comparing CoCoRaHS Data to Raw Radar Estimates We need more CoCoRaHS rainfall observers!

37 Why is flood forecasting so difficult? Now that we have a good estimate of rainfall

38 Why is flood forecasting so difficult? the problem is not solved! Runoff is not known!

39 Why is flood forecasting so difficult? Hydrologists simulate runoff using a model. The problem with the model is that the amount of moisture in the ground is not measured!

40 Why is flood forecasting so difficult? Hydrologists use a soil moisture accounting model to assess daily how much moisture is in the ground. Runoff is dependent on how wet or dry the soil is. Soil type, rainfall rate and duration, length of time between rain events, and the amount of evapotranspiration (time of year) are all factors in determining soil moisture.

41 Why is flood forecasting so difficult? Once soil saturation occurs and runoff begins, we can measure the amount through the stream gauging network in our rivers and make flood forecasts.

42 Why is flood forecasting so difficult? And once the water is in the rivers, lakes and topsoil, the whole cycle starts all over again!

43 Questions?

44 Thank You n Greg Story u Meteorologist u u greg.story@noaa.gov

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