Sciser Roadmap Hydro

SciSer_roadmap_hydro.doc Version 2002/08/27 Hydrologic Services (including Quantitative Precipitation Estimation, Quantitative Precipitation Forecasting, and Flood Forecasting) Science and Technology Infusion Plan FY 03 - FY 12

NWS Mission/Mandates/Major Requirements:

The National Weather Service is charged with providing weather, water, and climate data, warnings and forecasts to protect life and property and to enhance the economy. The water-related mission of the NWS is supported by the quantification of how much precipitation has fallen (QPE) and how much is forecast to fall in the future (QPF). Together these constitute quantitative precipitation information (QPI). This information is used as input to hydrologic forecast models that predict river stages to warn the public against river floods and flash floods and to aid in decision making for water managers. Science and technology enhancements related to QPI and flood forecasting are needed to improve the quantification and reduce biases of observed and forecasted precipitation over time and space scales ranging from minutes to months and several square miles up to national scales so that river stage forecasts over these same time- space scales are correspondingly more reliable and timely.

Primary External Customers, Partners, and Needs: Customer/Partner Information Need FEMA/Local emergency managers Past and predicted rainfall amount on scales from minutes to days. Current and predicted stage and flow Water resource managers (e.g. dam/hydroelectric plant operators) Past and predicted rainfall amount on scales from minutes to months. Current and predicted stage and flow Department of Agriculture/Farmers Past and predicted rainfall amount on scales from hours to months. Current and predicted stage and flow Construction companies Predicted occurrence or non-occurrence of rain during next few days Transportation ( Rail, trucking, bus etc.) Past and predicted rainfall amount on scales from minutes to days Municipal public works departments Predicted snowfall/ice amount on scales from hours to days School districts, day-care centers, local governments, airports Predicted snowfall/ice amount on scales from hours to days Universities Current and predicted stage and flow U.S. Army Corps of Engineers Current and predicted stage and flow, and reservoir inflow U.S. Geological Survey Current and predicted stage and flow U.S Bureau of Reclamation Current and predicted stage and flow, and reservoir inflow Department of Environmental Quality Current and predicted stage and flow U.S. Coast Guard Current and predicted stage and flow State Highway Departments Current and predicted stage and flow Water transportation/Barge operators/Navigation operators Current and predicted stage and flow Recreational users Current and predicted stage and flow

Major Operational Products and Services: Products/Services Provider River and flash flood forecasts, watches, and warnings WFOs, RFCs River Flood Outlooks and Significant Flood Outlooks RFCs River flow forecast histograms, fcst flow prob. of exceedence RFCs Satellite-based QPE products NESDIS/SAB and NESDIS/ORA Hourly WSR-88D+raingauge QPE products RFCs Daily and hourly raingauge data NWS 6- and 24-hrly QPFs for Days 1-2 NCEP/HPC Value-added 6-hrly QPF for Day 1 RFCs 24-hr QPF for Day 3 NCEP/HPC 48-hr QPF for Days 4-5 NCEP/HPC QPF total Days 1-5 NCEP/HPC Excessive Rainfall Potential Outlook NCEP/HPC QPF Discussion and Heavy Snow Discussion NCEP/HPC Hydrometeorological Discussion RFCs MOS value-added QPF (point values and LAMP) MDL Regional value-added gridded QPF products for days 1-2 RFCs River and Reservoir Stage and Flow RFCs Potential evaporation forecasts NCEP Freezing level forecasts NCEP Temperature forecasts NCEP and WFOs 1-, 3-, and 6-hr Flash Flood Guidance products RFCs Palmer Drought Severity Index NCEP/CPC Crop Moisture Index NCEP/CPC

Vision for Hydrologic Services, NWS Today (FY 03 – 07) and Next (FY 08 – 12):

Provide probabilistic-based precipitation products, both observed and forecasted, and river stage forecasts with reduced biases and higher resolution over time and space scales ranging from minutes to months and several square miles up to national scales to support current and future customer needs. Synopsis of Current/Proposed NWS Hydrology-related GPRA / Key Strategic Plan Performance Measures and Goals: FY01 FY07 FY07 FY12 FY12 Perf. Measures Actual Threshold Goal Objective Goal Threshold Goal Objective Goal QPF skill (Day 1 1-inch threat 0.261 0.244 0.263 0.255 0.274 score) Flash flood warning skill 0.86, 0.37, 47 mins. 0.89, 0.35, 48 mins. 0.92, 0.32, 55 mins. 0.93, 0.30, 60 mins. 0.95, 0.28, 65 mins. (POD,FAR,lead time) River flood warning skill No data available 0.80, 0.26, 7 hrs. 0.90, 0.18, 10 hrs. 0.85, 0.20, 8 hrs. 0.95, 0.15, 12 hrs. (POD,FAR,lead time)

Threshold = Within Programmed (Planned Base) Resources Only. Definition of Programmed: For FY 03-07: Contained within NOAA OMB 5-year plan; For FY 08-12: FY 07 Base Adjusted for Inflation. Leveraged resources can be considered Programmed if 1) resources are contained within sponsoring agencies OMB plan, 2) NOAA can legally utilize results, and 3) NOAA has resources to transition or make use of results. Objective = Within Budget Cap Performance Measure 1: QPF SKILL

A) Performance Measure History and Goals:

B) If this is a new performance measure, provide rationale for adopting it:

1) Day 1 QPF is a more important input to NWS River Forecast System (NWSRFS) than Day 3 QPF. 2) Threat score is a more hydrologically appropriate and balanced measure of skill than POD C) Provide rationale if proposing to discontinue current performance measure beyond FY 07:

Day 1 QPF is a more important input to NWS River Forecast System (NWSRFS) than Day 3 QPF. This is because there is much less uncertainty in the day 1 QPF and thus more reliability for using the day 1 QPF in the river model.

D) Social and economic benefits of reaching threshold and objective 2007 and 2012 goals:

Improved precipitation forecasts lead to improved hydrologic forecasts and warnings, reduced loss of life and property, and improved benefits to water managers. This is true for forecasting the volume of stream flow but not necessarily the peak flow. Without higher temporal frequency QPFs it will be difficult to relate the improvements in 24-hr QPF to improvements in flood forecasts. Improvements in surface water management can lead to improvements in river and Great Lakes transportation/navigation, more efficient production of hydropower, mitigation of environmental hazards due to runoff (e.g., fertilizer from golf courses, waste runoff from pig farms, etc.).

E) Description of performance measure: What is it? How is it verified?

Skill of Day 1 QPF is measured using threat score. Threat scores measure the skill in forecasting not only the location of 24-hr rainfall exceeding 1 inch but also correctly identifying the areal coverage. The measure is calculated for the conterminous U.S. for the period 12 to 36 hr into the future (range from 0 to 1 with 1 being perfect).

The verification scheme uses 24 hour observed rain gauge rainfall amounts that are assimilated by the River Forecasts Centers from the ASOS and Cooperative Observing Network. The 1 inch isohyet is manually drawn by an HPC Lead Forecaster with the help of a graphical 24-hr radar observed 24 h rainfall. This is used to better represent the area coverage. The spatial resolution of this analysis is approximately 32 km.

Skill of Day 3 QPF is measured via probability of detection (POD) of those areas within the conterminous U.S. observing precipitation of 1 inch or greater in the 24-h period 60 h to 84 h into the future. This uses the same manually derived isohyetal analysis as day 1.

F) Estimate of resolution, accuracy, location, timing, and other attributes of CRITICAL observed and forecasted geophysical information (parameters, features, processes) deemed sufficient to reach the THRESHOLD goals.

Critical Geo Info Crit. (parameter, feature, Thresholds Where? When? Accuracy, Value Key Solutions process) Precision, HML Red – mISSING OR INADEQUATE Confidence yELLOW – pARTIAL sOLUTION gREEN – mEETS nEED

Hor. H. Ver. V. Scale Range Freq. OBS MODELS LIMITING FACTORS Scale FY CONUS, 10 km 0-500 500 m 36 hr hourly ±20% (v), H Hygrometer NA-WRF-8 07 AK, HI, hPa ±50% (l,i) (sonde, sfc); Reg. Ens- H.Scale, freq (2x/day),accuracy Atmospheric nearby IR & WRF-18 water content waters microwave- Enh. (vapor, liquid, derived TPW, Microphys. ice) Td profiles, and liquid/ice path; WSR- 88D VIL FY CONUS, 5 km 0-500 250 m 36 hr 30 min ±20% (v), H Same as’ 07 NA-WRF-4 w/ V.scale 12 AK, HI, hPa ±50% (l,i) plus 4DVAR H&V scale, freq(2x/day), acc. nearby hyperspectral Reg.Ens-WRF- waters satellite Td 12 profiles, GPS; Enh microphys, dual-pol radar impr. cloud anal w/ dual pole radar, NPP satellite prods FY CONUS, 10 km 0-500 500 m 36 hr hourly ±1 m/s H Anemometer NA-WRF-8 H.Scale, freq (2x/day),accuracy 07 AK, HI, hPa (sfc, sonde); Reg. Ens- Horizontal nearby WSR-88D; WRF-18 and vertical waters GOES Enh. wind cloud/vapor- Microphys, components drift winds; 3dVAR w/ level wind profilers 2 radar superobs, AIRS sat. FY CONUS, 5 km 0-500 250 m 36 hr 30 min ±1 m/s H Same as ’07 NA-WRF-4 V.scale 12 AK, HI, hPa with higher- w/4DVAR H&V scale, freq(2x/day), acc. nearby resolution Reg.Ens-WRF- waters WSR-88D 12 and GOES Enh. Microphys, cloud analy w/ dual pole radar, NPP satellite prods FY CONUS, 10 km 0-500 500 m 36 hr hourly ±1 K H Thermometer NA-WRF-8 H.Scale, freq (2x/day),accuracy 07 AK, HI, hPa (sfc, sonde); Reg. Ens- Temperature nearby GOES/POES WRF-18 waters T profiles Enh. Microphys FY CONUS, 5 km 0-500 250 m 36 hr 30 min ±1 K H Same as ’07 NA-WRF-4 V.scale 12 AK, HI, hPa plus RASS w/4DVAR H&V scale, freq(2x/day), acc nearby Reg.Ens-WRF- waters 12 Enh. Microphys Cloud anal. w/ dual pole radar, NPP satellite prods FY CONUS, 10 km Sfc 36 hr hourly ±30% M ALEXI- NA-WRF-8 H.Scale, freq (2x/day),accuracy 07 AK, HI, retrieved Reg. Ens- Surface heat nearby surface WRF-18 and moisture waters heat/moisture N-LDAS fluxes fluxes using hyperspectral data FY CONUS, 5 km Sfc 36 hr 30 min ±30% M Same as ’07 NA-WRF-4 H scale, freq(2x/day), acc 12 AK, HI, with greater w/4DVAR nearby use of Reg.Ens-WRF- waters hyperspectral 12 data N-LDAS, imp. Precip obs & cpld hydro basin mdls FY CONUS, 4 km Sfc Prev. Hourly ±30% H Gauges; N/A 07 AK, HI, WSR-88D Observed nearby merged with precipitation waters other (for model networks; 4DDA) mobile radars; satellite; multisensor blend FY CONUS, 2 km Sfc Prev. 30 min ±30% H Same as ’07 N/A 12 AK, HI, 24 hr plus dual-pol nearby and phased- waters array radars; hypserpectral and GPM satellite QPE FY CONUS, 10 km 0-5 km Standard 1 hr hourly M Same as N/A Current 07 AK, HI profile observed location of precip precipitation FY CONUS, 10 km 0-5 km Standard 1 hr hourly M Same as N/A and clouds 12 AK, HI profile observed precip

G) Will the FY 07 THRESHOLD goal be met with programmed S&T and NON-S&T (e.g., training, ops concepts, human factors, etc.) advances alone?

50% probability that this threshold will be met based on 40 year historical record given long term improvements in numerical guidance. Natural variability in rainfall and year to year variations in predictability is what lowers the probability for any given year. The fact is this performance measure can be seriously impacted by summer convection. Without any organized synoptically forced warm season events there is little skill in forecasting the 1 inch isohyet during the 6 months April through September. There are more square degrees of 1 inch rainfall in the warm season than in the cool season. Thus we run the risk of missing this performance measure simply by year to year variations in a few large organized events. Thus we should not use individual year values, but the regression line instead. This is why the measure shows such slow improvement. If yes, list (with rationale) the programmed S&T advances (including enabling technology/infrastructure), which will deliver the geophysical information sufficient to reach the threshold goal. These advances should be reflected in your roadmaps (spreadsheets).

Consistent improvements in numerical model guidance that will come with improved data assimilation schemes and improved model resolution. This will allow less dependency on parameterization schemes such as sub-grid scale convection which should improve warm season precipitation QPF forecasts that will be the largest contributor to improving annual Day 1 1-inch threat scores. The current NCEP plan should provide the needed improvements to continue the slow but consistent improvements in Day 1 QPF.

Other enhancements that will improve QPF skill include: - ensemble NWP - NWP modeling with explicit microphysics and assimilation of multi-sensor data - coupling of atmospheric and hydrologic models

If no,

1) List (with rationale) the programmed S&T advances (including enabling technology/infrastructure), which will contribute to delivering the geophysical information sufficient to reach the threshold goal. These advances should be reflected you your spreadsheets.

2) What goal would the programmed-only advances support?

3) What are the gaps? 3.1) Identify the outstanding geophysical information sufficient to reach the threshold goal that WILL NOT be delivered by programmed S&T and recommended S&T solutions and costs. These additional advancements should be reflected in your spreadsheets.

3.2) Identify any outstanding NON-S&T needs (training, ops concepts, human factors, etc.) sufficient to reach threshold goals.

Statistically post-processed ensemble forecasts using a variety of regional models running at 5 km (maybe 5 different models each running 10 perturbations) would provide much improved guidance. This simply requires computer resources and the calibration of the output to provide reliability measures for the forecasters. It may be that the RFC hydrologist would be more inclined to use all the day 1 QPF if there was high statistical reliability in the QPF.

H) Estimate of additional observed and forecasted geophysical information attributes (see F) deemed sufficient to reach OBJECTIVE goals: Critical Geo Info Crit. (parameter, feature, Thresholds Where? When? Accuracy, Value Key Solutions process) Precision, HML Red – mISSING OR INADEQUATE Confidence yELLOW – pARTIAL sOLUTION gREEN – mEETS nEED

Hor. H. Ver. V. Scale Range Freq. OBS MODELS FCST TECH Scale FY CONUS, 10 km 0-500 500 m 36 hr hourly ±20% (v), H 07 AK, HI, hPa ±50% (l,i) Atmospheric nearby water content waters (vapor, liquid, FY CONUS, 5 km 0-500 250 m 36 hr 30 min ±20% (v), H ice) 12 AK, HI, hPa ±50% (l,i) nearby waters FY CONUS, 10 km 0-500 500 m 36 hr hourly ±1 m/s H 07 AK, HI, hPa Horizontal nearby and vertical waters wind FY CONUS, 5 km 0-500 250 m 36 hr 30 min ±1 m/s H components 12 AK, HI, hPa nearby waters FY CONUS, 10 km 0-500 500 m 36 hr hourly ±1 K H 07 AK, HI, hPa Temperature nearby waters FY CONUS, 5 km 0-500 250 m 36 hr 30 min ±1 K H 12 AK, HI, hPa nearby waters FY CONUS, 10 km Sfc 36 hr hourly ±30% M 07 AK, HI, Surface heat nearby and moisture waters fluxes FY CONUS, 5 km Sfc 36 hr 30 min ±30% M 12 AK, HI, nearby waters FY CONUS, 4 km Sfc Prev. Hourly ±30% H 07 AK, HI, Observed nearby precipitation waters (for model FY CONUS, 2 km Sfc Prev. 30 min ±30% H 4DDA) 12 AK, HI, 24 hr nearby waters FY CONUS, 10 km 0-5 km Standard 1 hr hourly M Current 07 AK, HI profile location of precipitation FY CONUS, 10 km 0-5 km Standard 1 hr hourly M and clouds 12 AK, HI profile I ) List (with rationale) recommended potential S&T advances (including enabling technology/infrastructure) beyond those listed in F, which if implemented might provide geophysical information in necessary to reach OBJECTIVE goals. These potential advances should be reflected in your spreadsheets.

-4-D variational assimilation to utilize observations -Explicit convection and microphysics in 4 km or higher resolution models -Tune models to handle heavier rainfall amounts, not the lower values -Develop more robust short range ensemble forecasts using high resolution multi-model approach with statistical post-processing of output to define deterministic amounts as well as confidence or probabilities. -Better tracks on land-falling tropical storms to errors of less than 20 nmi in 24-hr forecast.

J) Description/rationale of outstanding non-S&T needs (training, ops concepts, human factors, etc.) sufficient to reach objective goals:

As models improve and statistical post-processing methods improve, it will be more difficult for forecasters to add value. We will need to develop methods for forecasters to interact with ensembles to modify both the ensemble mean and probability distributions.

K) List potential S&T advancements and supporting rationale which may contribute to goals beyond objective above cap:

-Better understanding of MCS development and tracks and possibly parameterization of precipitation based on life cycle of system -Better forecasts of rapid cyclogenesis and tracks of major cool season events. This could have significant impact on 1 inch isohyet forecast -Projects that provide targeted observations like THORPEX, PACJET, boundary layer wind profiler network -Develop methods for manual bogusing of observations in high resolution numerical models that can be re-run on an interactive basis by the forecasters over areas expected to develop heavy rain. Bogusing would help weight important observations that initial model run missed. These methods might help better locate heaviest precipitation over next 6 to 18 hrs.

L) Revise NWS R&D Needs Document (see OST/SPB website) for this service-science area. Build in description/rationale of outstanding geophysical information needs (beyond what known advances will deliver) sufficient to meet goals. Performance Measure 2: FLASH FLOOD WARNING SKILL

A) Performance Measure History and Goals (Threshold-red dashed curve and Objective- ): B) If this is a new performance measure, provide rationale for adopting it:

Not applicable.

C) Provide rationale if proposing to discontinue current performance measure beyond FY 07:

Not applicable.

D) Social and economic benefits of reaching threshold and objective 2007 and 2012 goals: Floods are responsible for more deaths than any other severe weather phenomenon. Improved detection and forecasting of impending events will lead to improved lead-time for the public and governments to take action to protect life and property. Increased trust in NWS warnings. Reduced false alarms.

E) Description of performance measure: What is it? How is it verified?

POD, FAR, lead time

What is it? The lead time for a flash flood warning is the difference between the time the warning was issued and the time the flash flood affected the area for which the warning was issued. The lead times for all flash flood occurrences throughout the year are averaged to get this statistic. The accuracy of the warnings is measured by the percentage of times a flash flood actually occurred in an area already covered by a warning. The false alarm rate is measured by

How is it verified? The data source is from the NWS Forecast Offices which report monthly. Data are stored at the NWS Office of Climate, Water, and Weather Services (OS) in Silver Spring, MD.

Verification is the process of comparing the predicted flash flooding to the actual event. The process begins with the collection of warnings from every NWS Forecast Office across the Nation. The flash flood program includes extensive quality control procedures to ensure the highest reliability of each event report. The data in each report are entered into a database which contains flash flood warnings. The warnings and events are matched, and appropriate statistics are calculated and made available to all echelons of the NWS.

Critical Geo Info Cr (parameter, feature, it. Where? When? Accur Val Key Solutions process) Th acy, ue Red – mISSING OR INADEQUATE re Precis HM yELLOW – pARTIAL sOLUTION sh ion, L gREEN – mEETS nEED ol Confi ds dence

Hor. H. Ver. V. Range Freq. OBS MODELS LIMITING FACTORS Scale Scale FY 07 CONUS, AK, 4 km Surface Surfa Prev. 10 +-30% H Gauges; N/A HI ce 6 hr min WSR-88D QPE merged with other networks; mobile radars; satellite; multisensor blend FY 12 CONUS 1 km Surface Surfa Prev. <5 +-30% H Same as ’07 N/A AK, HI ce 6 hr min plus dual-pol and phased- array radars; hypserpectral and GPM satellite QPE FY 07 CONUS, AK, 4 km Surface Surfa 0-3 hr 15 ±100% H RRW-11k, impr. Freq (16x/day), H. Scale, no aK, HI HI ce min Microphys., 3 QPF DVAR, radar refl. Assim into cloud anal FY 12 CONUS, AK, 2 km Surface Surfa 0-3 hr 5 min ±100% H RRW-7k, dual pole, Freq (16x/day), H. Scale, No AK, HI HI ce NPP 4DVAR, imp. cloud anal, . microphys FY 07 CONUS, AK, 2 km 0-100 cm Surfa Prev. 6 hr H Palmer index, N-LDAS-8k H. Scale, acc HI below ce 24 hr API, in-situ Impr. Obs precip Soil surface 0-3 hr sensors, cpling moisture AMSR soil moisture, ALEXI available soil water FY 12 CONUS, AK, 1 km 0-100 cm Surfa Prev. 1 hr H Same as ’07 N-LDAS-4km H. Scale, acc HI below ce 24 hr with additional 4DVAR, Impr. surface 0-6 hr in-situ and Obs. Precip & microwave hydro. Basin sensors cplng, Hydrologic FY 07 CONUS, AK, Surface Surfa 0 hr As H AVHRR NDVI; N/A and HI ce neede POES land hydraulic d use/vegetation parameters ; soil survey (topography maps; , channel improved GIS geometry, FY 12 CONUS, AK, Surface Surfa 0 hr As H Same as ’07 N/A channel HI ce neede with POES roughness, d instrument soil resolution; properties, additional GIS land use, improvements land cover) FY 07 CONUS, AK, Surface Surfa 0-3 hr M N/A ??? (See M. HI ce Smith) Streamflow and stage FY 12 CONUS, AK, Surface Surfa 0-6 hr M N/A ??? (See M. HI ce Smith) FY 07 CONUS, AK, 4 km Surface Surfa 1 hr +-30% M N/A HI ce Climatologic al QPE FY 12 CONUS, AK, 1 km Surface Surfa 5 min +-30% M N/A HI ce

G) Will the FY 07 THRESHOLD goal be met with programmed S&T and NON-S&T (e.g., training, ops concepts, human factors, etc.) advances alone?

FY07 threshold goals will not be met unless we connect closely with other ongoing and proposed programs such as the USWRP QPF, data assimilation and the optimal mix, hurricane landfall programs, Climate and Global Change Programs, field type experiments such as the Pacific Landfalling Jets Experiment (PACJET) and IHOP, and the upcoming NASA Global Precipitation Mission (GPM) --- the follow on to TRMM (Tropical Rainfall Measurement Mission). The above does not cover all of the programs (both current and planned) that we could interact with in order to receive additional/essential funding. Connecting and being funded by these programs will allow for resources that would be used for new cutting edge concepts (expert systems for QPE and QPF; microwave on a GOES platform; etc) that otherwise would not be covered by normal funding channels.

A caveat in all of this is training that is extremely important and really essential is often the first program to be cut during times of tight resources. We must be innovative in our ways to insure that training, human factors and communications with the users and decision makers is accomplished.

It is possible the FY07 THRESHOLD goals are attainable with the current technology available from researchers within NOAA and from the private sector. However, it is not likely the THRESHOLD goals will be attained with the current level of funding that will preclude the necessary testing, integration, maintenance infrastructure, and training needed to support operations.

If yes, list (with rationale) the programmed S&T advances (including enabling technology/infrastructure), which will deliver the geophysical information sufficient to reach the threshold goal. These advances should be reflected in your roadmaps (spreadsheets).

- Ensemble/probabilistic QPE algorithms and products - Improved physics in hydrologic and hydraulic modeling - Distributed hydrologic and hydraulic modeling - Improved parameter estimation - Real-time assimilation of hydrologic and hydrometeorological data - Statistical post-processing of QPF (including downscaling) and model output - Algorithms such as MPE (NWS/OHD) and QPE-SUMS (OAR/NSSL) for estimating precipitation - Runoff models such as Vflo (Vieux and Associates, Inc.) - Integration with mesonet surface observation networks available around the country (e.g., Oklahoma Mesonet) - Dual polarized radar data (OAR/NSSL) - Small scale, high resolution numerical models (OU/CAPS ARPS model that integrates WSR-88D radar data, MM5 20 km, LAPS, etc.) - Data assimilation techniques such as ADAS and 4DVAR

If no,

2) What goal would the programmed-only advances support?

Given the current level of funding and time required to transfer technology from research into operations, it is possible off-line prototype testing (or perhaps regional testing within operations) of the technology listed in (1) could be attainable by FY 07.

3) What are the gaps? 3.3) Identify the outstanding geophysical information sufficient to reach the threshold goal that WILL NOT be delivered by programmed S&T and recommended S&T solutions and costs. These additional advancements should be reflected in your spreadsheets.

3.4) Identify any outstanding NON-S&T needs (training, ops concepts, human factors, etc.) sufficient to reach threshold goals.

H) Estimate of additional observed and forecasted geophysical information attributes (see F) deemed sufficient to reach OBJECTIVE goals:

Hor. H. Ver. V. Scale Range Freq. OBS MODELS FCST TECH Scale FY CONUS, 1 km Surface Surface Prev. 5 min +-10% H AK, HI 6 hrs QPE FY CONUS, 0.5 km Surface Surface Prev. <2.5 +-5% H 12 AK, HI 6 hrs min

FY CONUS, 2 km Surface Surface 0-24 15 +- 15% H 07 AK, HI hr min QPF FY CONUS, 1 km Surface Surface 0-24 <5 <+-10% H 12 AK, HI hr min

FY CONUS, 0.5 km 0-100 cm Surface Prev. 5 min +_10% H 07 AK, HI below 24 hr Soil surface 0-3 hr moisture FY CONUS, 0.25 0-100 cm Surface Prev. 2.5 +_5% H 12 AK, HI km below 24 hr min surface 0-6 hr

I ) List (with rationale) recommended potential S&T advances (including enabling technology/infrastructure) beyond those listed in F, which if implemented might provide geophysical information in necessary to reach OBJECTIVE goals. These potential advances should be reflected in your spreadsheets.

GPM, mentioned above as the TRMM follow on, will enhance the QPE algorithms but will be available on only a 3 hour basis (GPM rainfall measurements will come from polar orbiting satellites). Thus S&T advances need to include the best/cutting edge methods of combining radar, 1 - 15 minute GOES data, and other data sources with GPM. Of course rain gauge calibrated radar in many cases is the best source of information but does not cover the entire CONUS and surrounding ocean and land areas where satellite information is a necessity.

Phased Array Radar (PAR) will reduce volume scan time from 6 min to 1 min with the ability to substantially reduce data processing time.

J) Description/rationale of outstanding non-S&T needs (training, ops concepts, human factors, etc.) sufficient to reach objective goals:

Training on new cutting edge algorithms, concepts and processes may suffer in the threshold goal arena but should easily be achievable as an objective goal.

K) List potential S&T advancements and supporting rationale which may contribute to goals beyond objective above cap:

A potential S&T advancement is a microwave instrument aboard a GOES satellite. This would not only allow continuous microwave derived precipitation estimates but also more physically based water vapor measurements (as compared to infrared and visible data) that are so important to QPE and QPF.

Phased Array Radar (PAR) will reduce volume scan time from 6 min to 1 min with the ability to substantially reduce data processing time.

Gap filling radars such as TDWR, ASR, private sector, etc., that can be integrated with existing NWS WSR-88D radars. The key will be the data integration of these sensors with other sensors and model data (data assimilation challenge). L) Revise NWS R&D Needs Document (see OST/SPB website) for this service-science area. Build in description/rationale of outstanding geophysical information needs (beyond what known advances will deliver) sufficient to meet goals.

As the frequency and timeliness of satellite microwave increases, so will the opportunity to extend the accurate radar precipitation information to remote areas not covered by radar. Satellite microwave is more physically based than infrared and visible data and will allow for a more natural extension of the future polarized WSR-88D measurements to remote areas over the CONUS including AK and HI. Performance Measure 3: RIVER FLOOD WARNING SKILL

A) Performance Measure History and Goals (Threshold-red dashed curve and Objective- ): B) If this is a new performance measure, provide rationale for adopting it:

The new river flood warning performance goals will not be GPRA goals.

River flooding is one of the most damaging and expensive weather, water and climate-related disasters in the U.S. and an integral part of the NWS mission. Recent advances in science and technology has been improving the quality of river forecasts steadily and significantly, and quantitative performance measures are needed to systematically monitor, assess and project the progress.

C) Provide rationale if proposing to discontinue current performance measure beyond FY 07:

Not applicable. D) Social and economic benefits of reaching threshold and objective 2007 and 2012 goals:

Costs due to floods are ranked second only to hurricanes. Improved river flood warnings will result in reduced loss of life and property.

E) Description of performance measure: What is it? How is it verified?

What is it? Verification of WFO flood warnings is not a simple process, and has many more complicating factors than verification of warnings for severe weather (e.g., tornadoes, severe thunderstorms, and flash floods). The first complicating factor is that the verification system needs to handle both site-specific and areal flood warning products. The second complicating factor is that meaningful information must be provided on water courses ranging in size from small streams to large main-stem rivers.

The verification process will compute statistics for two warning product categories – site-specific flood warnings and areal flood warnings. For each of these warning product categories, statistics will also be stratified according to the three response time classes (fast, intermediate, slow). This makes a total of six groupings for which the following statistics will be calculated: probability of detection (POD), false alarm ratio (FAR), and lead time.

How is it verified? The data source for areal flood warnings will come from the NWS field offices which will report monthly. The data source for site-specific flood warnings will be computed based on data gathered from 10,000 gages provided by the USGS, Corps of Engineers and other cooperators. Data will be stored at the NWS Office of Climate, Water, and Weather Services (OS) in Silver Spring, MD.

Verification is the process of comparing the predicted flooding to the actual event. The process will begin with the collection of warnings from every NWS forecast office across the Nation. The river flood warning program will include extensive quality control procedures to ensure the highest reliability of each event report. The data in each report will be entered into a database which contains river flood warnings. The warnings and events will be matched, and appropriate statistics will be calculated and made available to all echelons of the NWS.

Critical Geo Info Crit. (parameter, feature, Thr Where? When? Accur Val Key Solutions process) esh acy, ue Red – mISSING OR INADEQUATE old Precis HM yELLOW – pARTIAL sOLUTION s ion, L gREEN – mEETS nEED Confi dence

Hor. H. Ver. V. Range Freq. OBS MODELS LIMITING FACTORS Scale Scale QPE FY CONUS, AK, 4 km surface Prev. 6 min H Gauges; N/A (including HI 24 hr WSR-88D solid and merged with liquid other phases) networks; (deterministi mobile radars; c and satellite; ensemble) multisensor blend FY CONUS, AK, 1 km surface Prev. 3 min H Same as '07 N/A 12 HI 24 hr plus dual-pol and phased- array radars; hypserpectral and GPM satellite QPE QPF FY CONUS, AK, 10 km surface 0-84 6 hrs H WRF-8k, impr. (including 07 HI hrs Microphys., 3 solid and DVAR, radar refl. liquid Assim into cloud phases) anal (deterministi Reg.Ens.-WRF-18 c and k ensemble) FY CONUS, AK, 10 km surface 0-84 6 hrs H NA-WRF-4 w/ 12 HI hrs 4DVAR Reg.Ens-WRF-12 Enh microphys, impr. cloud anal w/ dual pole radar, NPP satellite prods Temperature FY CONUS, AK, 10 km surface 0-84 6 hr H WRF-8k, impr. forecasts 07 HI hrs Microphys., 3 (deterministi DVAR, radar refl. c and Assim into cloud ensemble) anal Reg.Ens.-WRF-18 k FY CONUS, AK, 10 km surface 0-84 6 hr H NA-WRF-4 w/ 12 HI hrs 4DVAR Reg.Ens-WRF-12 Enh microphys, impr. cloud anal w/ dual pole radar, NPP satellite prods Potential FY CONUS, AK, 10 km surface 0-84 6 hr M NA-WRF-8 Accuracy unknown evaporation 07 HI hrs Reg. Ens-WRF-18 (observed N-LDAS w/ obs and precip cpling estimated) FY CONUS, AK, 10 km surface 0-84 6 hr M NA-WRF-4 Accuracy unknown 12 HI hrs w/4DVAR Reg.Ens-WRF-12 N-LDAS, imp. Precip obs & cpld hydro basin mdls Soil FY CONUS, AK, 4 km 0-100 cm Prev. 6 hr M Palmer index, NA-WRF-8 Accuracy unknown temperature 07 HI below the 24 hr API, in-situ Reg. Ens-WRF-18 and surface 0-48 sensors, N-LDAS w/ obs moisture hr AMSR soil precip cpling content moisture, ALEXI available soil water FY CONUS, AK, 4 km 0-100 cm Prev. 1 hr M Same as ’07 NA-WRF-4 Accuracy unknown 12 HI below the 24 hr with additional w/4DVAR surface 0-72 in situ and Reg.Ens-WRF-12 hr microwave N-LDAS, imp. sensors Precip obs & cpld hydro basin mdls Hydrologic FY CONUS, AK, Surface Surfa 0 hr As H AVHRR NDVI; N/A and 07 HI ce neede POES land hydraulic d use/vegetation parameters ; soil survey (topography maps; , channel improved GIS geometry, FY CONUS, AK, Surface Surfa 0 hr As H AVHRR NDVI; N/A channel 12 HI ce neede POES land roughness, d use/vegetation soil ; soil survey properties, maps; land cover, improved GIS land use) Snow depth FY CONUS, AK 4 km Prev. 6 hr H and liquid 07 24 hr water 0-48 equivalent hr FY CONUS, AK 4 km Prev. 6 hr H 12 24 hr 0-72 hr Groundwate FY CONUS 4 km To ground- 0 hr M See. M. Smith r level 07 water table

FY CONUS 4 km To ground- 0 hr M 12 water table

Streamflow FY CONUS, AK, Prev. 1 hr H See M. Smith and stage 07 HI 72 hr 0-3 days FY1 CONUS, AK, Prev. 1 hr H 2 HI 72 hr 0-7 days Climatologic FY CONUS, AK, 4 km Surface Surfa 1 hr +-30% M N/A al QPE, 07 HI ce QPF, and potential FY CONUS, AK, 1 km Surface Surfa 5 min +-30% M evaporation 12 HI ce G) Will the FY 07 THRESHOLD goal be met with programmed S&T and NON-S&T (e.g., training, ops concepts, human factors, etc.) advances alone?

- Ensemble/probabilistic QPE algorithms and products - Improved physics in hydrologic and hydraulic modeling - Distributed hydrologic and hydraulic modeling - Snow modeling - Frozen ground modeling - Improved parameter estimation - Real-time assimilation of hydrologic and hydrometeorological data - Ensemble hydrologic forecasting - Statistical post processing of model input - Statistical post processing of model output

If no,

2) What goal would the programmed-only advances support?

3) What are the gaps? 3.5) Identify the outstanding geophysical information sufficient to reach the threshold goal that WILL NOT be delivered by programmed S&T and recommended S&T solutions and costs. These additional advancements should be reflected in your spreadsheets.

3.6) Identify any outstanding NON-S&T needs (training, ops concepts, human factors, etc.) sufficient to reach threshold goals.

H) Estimate of additional observed and forecasted geophysical information attributes (see F) deemed sufficient to reach OBJECTIVE goals: Critical Geo Info Crit. (parameter, feature, Thr Where? When? Accur Val Key Solutions process) esh acy, ue Red – mISSING OR INADEQUATE old Precis HM yELLOW – pARTIAL sOLUTION s ion, L gREEN – mEETS nEED Confi dence

Hor. H. Ver. V. Range Freq. OBS MODELS FCST TECH Scale Scale QPE FY CONUS, AK, 4 km surface Prev. 6 min H (including 07 HI 24 hr solid and liquid FY CONUS, AK, 1 km surface Prev. 3 min H phases) 12 HI 24 hr (deterministi c and ensemble) QPF FY CONUS, AK, 10 km surface 0-84 6 hrs H (including 07 HI hrs solid and liquid FY CONUS, AK, 10 km surface 0-84 6 hrs H phases) 12 HI hrs (deterministi c and ensemble) Temperature FY CONUS, AK, 10 km surface 0-84 6 hr H forecasts 07 HI hrs (deterministi c and FY CONUS, AK, 10 km surface 0-84 6 hr H ensemble) 12 HI hrs

Potential FY CONUS, AK, 10 km surface 0-84 6 hr M evaporation 07 HI hrs (observed and FY CONUS, AK, 10 km surface 0-84 6 hr M estimated) 12 HI hrs

Soil FY CONUS, AK, 4 km 0-100 cm Prev. 6 hr M temperature 07 HI below the 24 hr and surface moisture FY CONUS, AK, 4 km 0-100 cm Prev. 1 hr M content 12 HI below the 24 hr surface

Hydrologic FY CONUS, AK, Surface Surfa 0 hr As H and 07 HI ce neede hydraulic d parameters FY CONUS, AK, Surface Surfa 0 hr As H (topography 12 HI ce neede , channel d geometry, channel roughness, soil properties, land cover, land use) Snow depth FY CONUS, AK 4 km Prev. 6 hr H and liquid 07 24 hr water equivalent FY CONUS, AK 4 km Prev. 6 hr H 12 24 hr

Groundwate FY CONUS 4 km To ground- 0 hr M r level 07 water table

FY CONUS 4 km To ground- 0 hr M 12 water table

Streamflow FY CONUS, AK, Prev. 1 hr H and stage 07 HI 72 hr

FY1 CONUS, AK, Prev. 1 hr H 2 HI 72 hr

Climatologic FY CONUS, AK, 4 km Surface Surfa 1 hr +-30% M al QPE, 07 HI ce QPF, and potential FY CONUS, AK, 1 km Surface Surfa 5 min +-30% M evaporation 12 HI ce

Combination of dense in-situ and remotely sensed (not necessarily satellite-born) observation of river channels for channel cross section and streamflow for fine-scale hydraulic modeling and for real-time updating of the hydrologic model state variables. Further advances in computational hydrology and hydraulics for parallel processing of distributed models and ensemble prediction. Integration of water use (both consumptive and non-consumptive) models into hydrologic models to better-account for the movement of water in the forecast system.

J) Description/rationale of outstanding non-S&T needs (training, ops concepts, human factors, etc.) sufficient to reach objective goals: Distributed models and ensemble prediction shift the operational forecasting paradigm from the current man-machine mix to the man-as-analyst mode, and hence require additional needs for training, development of operations concepts and human factors considerations to realize the full benefits expected from the S&T advances.

K) List potential S&T advancements and supporting rationale which may contribute to goals beyond objective above cap: Potentials exist that the communications technology may provide inexpensive ‘soft’ but dense and frequent observations of river conditions at the national scale. Then, with leaping advances in computing capabilities, potentials exist for ensemble implementation of coupled distributed model and real-time data assimilation systems in a massively parallel architecture.