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Improving Observations of Coastal Storms Allen White, NOAA/ESRL Boulder, CO With key contributions from Mike Dettinger (Scripps) and Dan Gottas, Seth Gutman,

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Presentation on theme: "Improving Observations of Coastal Storms Allen White, NOAA/ESRL Boulder, CO With key contributions from Mike Dettinger (Scripps) and Dan Gottas, Seth Gutman,"— Presentation transcript:

1 Improving Observations of Coastal Storms Allen White, NOAA/ESRL Boulder, CO With key contributions from Mike Dettinger (Scripps) and Dan Gottas, Seth Gutman, Paul Neiman, Marty Ralph, Tim Schneider, and Gary Wick (ESRL)

2 Purpose and Outline Purpose: To describe recent advances in observational research that have been developed to improve the detection and monitoring of key atmospheric phenomena associated with winter storms impacting California. Outline Part I: A new water vapor flux tool for monitoring atmospheric rivers over land Part II: CA DWR EFREP Program – Providing an HMT legacy for California Part III: UAS and the Pacific Testbed

3 Part I A new water vapor flux tool for monitoring atmospheric rivers over land

4 SSM/I satellite image of integrated water vapor (IWV) at 18UTC 16- Feb-04: AR landfall in N CA ~250 mm rain in 2 days Stream gauge rankings for 17- Feb-04 show regional extent of high streamflow covering roughly 500 km of coast All flood events on the Russian River (in N CA) in last 10 years tied to land-falling ARs atmospheric river Heavy cool-season rain & flood events along the U.S. West Coast are orographically driven and occur most often when narrow warm-sector corridors of strong water- vapor transport (i.e., atmospheric rivers – ARs) intersect the coastal mountains (e.g., Ralph et al in GRL; Neiman et al in JHM). Global reanalysis IVT (kg s -1 m -1 ): 16-Feb-04 IVT (kg s -1 m -1 )

5 Flood-prone Russian River Basin northwest of San Francisco: 2000/01, 2003/04, 2004/05, 2005/06 Analyses for when the following observing systems were simultaneously operating – (a) Bodega Bay (BBY): GPS-IWV unit, 915-MHz wind profiler, rain gauge (b) Cazadero (CZD): rain gauge Total rainfall: CZD = 4217 mm, BBY = 2016 mm 9548 hourly data points Upslope flow: orthogonal to the axis of the coastal mtns 30 km Neiman et al. (2008), Water ManagementNeiman et al. (2002), MWR Developed real-time monitoring of vapor transports to assess the orographic forcing, based on published research using wind profilers, as well as GPS receivers that measure IWV

6 Component of the flow in the orographic controlling layer directed from 230°, i.e., orthogonal to the axis of the coastal mtns All data points

7 Any rain: >0 m/s; >1 cm

8 Rain >5 mm/h: >6 m/s; >1.5 cm

9 Rain >10 mm/h: >12.5 m/s; >2 cm Atmospheric river quadrant: Strongest IWV fluxes (i.e., U 1km x IWV)* yield heaviest rains *Nearly 2/3 of tropospheric water vapor is in the lowest 2 km MSL. Hence, to first order, the IWV flux provides a close estimate of the low-level water-vapor transport into the coastal mountains.

10 Bodega Bay (BBY; 12 m MSL) Piedras Blancas (PPB; 11 m MSL) Goleta (GLA; 3 m MSL) Prototype WV flux tool tested at 3 couplets during NOAA’s HMT-2008 L BBY/CZD PPB/TPK GLA/SMC 0030Z 5-Jan-08: Intense western U.S. storm Coast (profiler, GPS, rain gauge): Cazadero (CZD; 475 m MSL) Three Peaks (TPK; 1021 m MSL) San Marcos Pass (SMC; 701 m MSL) Mountains (rain gauge): North: Central: South: Couplet land-falling atmospheric river

11 The top of three panels of the forecast tool displays hourly wind profiles and snow levels Current time Altitude in km Altitude in kft Observed winds: 24 hForecasted winds: 24 h Wind speed scale Controlling layer where upslope flow is calculated Forecasted melting level Observed bright- band snow level (White et al. 2002) Model: Advanced Research WRF (ARW), 48-h duration Grid configuration: 3 km horizontal, 30 vertical levels

12 The middle panel displays the upslope component of the flow and the IWV Observed IWVForecasted IWV Forecasted upslope flow Observed upslope flow Upslope scale Upslope direction defined IWV scale The thin horizontal lines define thresholds for IWV and upslope flow (2 cm and 12.5 m s -1 ; respectively) that were shown to produce heavy rain (Neiman et al. 2008)

13 The IWV and upslope flow from the middle panel are combined to produce a bulk IWV flux, which is displayed in the bottom panel along with the coastal and mountain hourly rainfall Forecasted IWV fluxObserved IWV flux Observed rainfall (bars): Red = coastal site Green = mountain site The thin blue horizontal line gives the IWV flux threshold (25 cm x m s -1 ) determined by multiplying the IWV and upslope flow thresholds defined in the middle panel Forecasted rainfall (T posts): Red = coastal site Green = mountain site

14 Northern couplet: BBY & CZD Orogr. forcing predicted well in this portion of the AR......but not the QPF, esp. in AR conditions. next slide focuses on bottom panel

15 Time of max. IWV flux at BBY: 1500 UTC 4-Jan-08 4 Jan 2008, 1500 UTC Time (UTC) CZD rain: 264mm BBY rain: 36mm 4 Jan 2008, 2100 UTC Time of max. IWV flux at PPB: 2100 UTC 4-Jan-08 Time (UTC) TPK rain: 320mm PPB rain: 75mm 5 Jan 2008, 0300 UTC Time of max. IWV flux at GLA: 0300 UTC 5-Jan-08 Time (UTC) SMC rain: 230mm GLA rain: 51mm AR Propagation: ~12 m s -1. ½-day lead time for SoCal Max. IWV flux in AR highly correlated with max. mountain rainfall at each site

16 Summary – Part I Ongoing research has led to the creation of a real-time vapor-flux tool to monitor orographic rainfall forcing at multiple coastal sites. By combining observations and forecast model output, users can see how well a forecast model represents land-falling ARs and their resulting impacts on orographic rainfall enhancement. For the case shown, the WRF model reasonably captured parts of the orographic forcing. However, the coastal and mountain rains were predicted poorly (due to microphysics & terrain resolution?), and orographic forcing in the AR lasted longer in the model than observed (not shown). The three monitoring couplets deployed along the CA coast provided valuable lead time to forecasters for conditions leading to extreme rainfall. Given the absence of alternative monitoring capabilities for low-level water vapor flux at the coast, consideration is being given to the operational implementation of the above tool to fill this gap.

17 A Weather & Water Insurance Policy for California Photo by Stephan Dietrich Part II CA Department of Water Resources (DWR) Enhanced Flood Response and Emergency Preparedness (EFREP) Program provides a legacy for NOAA’s Hydrometeorological Testbed (HMT) Program

18 21 st Century Observations Requirements/Drivers Flood Risks –Major basin hydrographs are more variable over last ~50 years Five highest flows on American River occurred since Folsom dam was built Similar results on Feather and San Joaquin Rivers –Earlier snowmelt combined with heavy spring storms raises flood risk Need to redefine probable maximum precipitation and include the impacts of rain on snow Water Resources –Uncertainty in storm intensity and annual rainfall will require adaptable water management strategies –Should CA invest in more storage capacity or reoperate current reservoirs using improved weather forecast information? (Forecast- Based Operations) Climate Change –25% reduction in snow pack by 2050 –Earlier snowmelt pushes peak runoff into winter storm period and stresses water supply during dry season

19 Lester Snow, CA-DWR

20 --> Storage & transferability of water supplies will thus be at a premium. Climate change may put some water managers in a real bind!

21 Next Generation Observations Four primary “Tiers” envisioned for next generation observations based on concept and technology maturity and feasibility: Tier-I: Well-defined needs, proven technology, low cost Tier-II: Well-defined needs, proven technology, moderate cost Tier-III: Needs assessment and technology prototype tests in HMT- West, high cost Tier-IV: Offshore aircraft reconnaissance, potentially very high cost/very high benefit

22 IV: Off- shore recon. Tier III: Newer technology Ex: Gap-filling radars, Buoy-mounted WPs Tier I: Address well-defined needs with proven technology Ex: Soil moisture sensors at CIMIS sites, GPS receivers of opportunity, snow-level radars Tier II: Expand on well-defined needs with proven technology Ex: Wind profilers, Coastal Atmospheric river observatory A tiered approach for nex gen obs to help address CA’s water resource issues

23 Soil moisture StreamflowCum. rainfall Russian River at Healdsburg Tier 1: Soil moisture monitoring at CIMIS sites Date Adding soil moisture to select California Irrigation Management Information System (CIMIS) and other mountain rain gauge sites will improve stream flow prediction and monitoring Using existing infrastructure provided by CIMIS network greatly reduces costs associated with installation

24 SIO (Scripps) proposed network of GPS receivers for geospatial applications (high resolution position mapping) Installing surface temperature and pressure sensors near these receiver sites will allow the network to map out the distribution of vertically integrated precipitable water vapor (IPW) Energy industry (electricity distribution) benefits because GPS receivers are used by Space Weather Center to monitor geomagnetic storms Tier 1: GPS receivers of opportunity

25 Snow level Tier 1: Snow level radars Provides precise snow-level height during precipitation events Utilizes proven FMCW technology to lower cost. Uses the patented ESRL automated snow-level detection algorithm (White et al. 2002) proven in CA field trials Less than 8’ diameter footprint Low-power requiring minimal infrastructure

26 Tier 1: Builds on existing networks and adds proven, low cost technologies: GPS-met Soil moisture Snow-level radars Receivers already exist Use existing CIMIS sites At major reservoirs Map of Tier I

27 CA GDP = $1.62 T $45k per capita National Profiler Network of Japan Tier 2: Proposed Profiler Network for CA Sea of Japan Pacific Ocean Japan = 374,744 sq mi CA = 155,959 sq mi 2005 data, sources: Wikipedia and U.S. Bureau of Economic Analysis GNP = $4.66 Trillion, $38k per capita California faces some of the same risks from winter storms that Japan faces with typhoons

28 Atmospheric River (AR) Observatory: Russian River Prototype Objectives: Monitor key AR and precipitation characteristics. Observing systems: 1.Wind profiler/RASS 2.S-band radar 3.Disdrometer 4.Surface met 5.GPS-IWV 6.Rain gauges 1. 2., Tier 2: Atmospheric River Observatory

29 Tier 1: Builds on existing networks and adds proven inexpensive technologies: GPS-met Soil moisture Snow-level radars Tier 2: Adds networks of proven, moderately expensive technologies: Wind profilers Atmos. River Observatories Providing more info aloft Map of Tier I-II

30 Summary – Part II A five-year Memorandum of Agreement has been signed by CA DWR and NOAA to bring 21 st century observation and modeling (not shown) capabilities to bear on the state’s flood protection and water resource management issues. The program will take advantage of existing state infrastructure to provide statewide networks of soil moisture and GPS integrated water vapor sensors. A new FM-CW S-band radar will provide critical measurements of the snow level during precipitation events to benefit a variety of end users. A west coast winter storms reconnaissance program is sorely missing. The NOAA Unmanned Aerial System (UAS) program may provide a viable platform for these missions.

31 ALTAIR at Channel Islands National Marine Sanctuary Part III Unmanned Aerial Systems and the Pacific Testbed

32 Background In 2005 NOAA formed an internal Unmanned Aerial Systems (UAS) Steering Committee and Working Group, which has subsequently identified a range of potential NOAA uses for UAS, including monitoring at-sea activities for fisheries and marine sanctuary enforcement purposes. Shown here are proposals for the possible future application of UAS technology in the accomplishment of NOAA’s missions in the Pacific.

33 Silver Fox in background on launcher and Manta in foreground

34 Humpback photographed from Silver Fox

35 Definition of UAS UAS = Unmanned Aerial System System is comprised of Subsystems: –Airframe (Platform) –Avionics and Communication: –Ground Control: –Launch and recovery –Payload: Sensors: –Scientific –Operational

36 Sensors (Operational) Optical Infrared Radar Hyperspectral AIS (Automatic Identification System) Etc…

37 Sensors (Scientific) Dropsondes (for atmospheric temperature and moisture profiles) Cloud radar (a UAS version is under development) Microwave radiometer (for atmospheric moisture; one flown on Altair) Atmospheric composition/atmospheric chemistry (eg. ozone) sensors

38 Why and where would we use UAS in NOAA? Missions that are : Dirty (e.g., flying over forest fires) Dull (e.g., searching for ocean debris) Dangerous (e.g., flying over the Arctic) Remote (e.g., long-endurance flights) Unique mission requirements: o Smaller and quieter UAS don’t disturb animals as much as a manned aircraft would o Stealth provides advantages for surveillance and enforcement o Persistence o Better data resolution o Can be quickly deployed and positioned

39 Unmanned Aircraft Systems have great potential to fill this void and take observations to complement our existing platforms Void between satellites and surface-based sensors

40 NOAA UAS Testbeds

41 Three testbeds are envisioned to be developed to support NOAA UAS experiments. They are nominally located in the Southeast, Alaska and the Pacific. The Southeast Testbed will be principally designed to support hurricane research and to support advanced severe weather prediction. The Alaska Testbed will aid in the study of the Arctic environment and global change research.

42 Pacific UAS Testbed Mission objectives encompassing weather, air quality monitoring, marine debris, marine mammal surveys, and fisheries enforcement Operations planned from NASA Dryden Flight Research Center and Barking Sands Pacific Missile Range Facility Targeting both low and high altitude demonstrations in 2009 Initial focus to be on winter storms and atmospheric rivers La Conchita Debris Flow Humpback whale photo from Silver Fox UAS Potential operating locations

43 Low Altitude Atmospheric Rivers Mission Characterize the moisture flux from the ocean surface underneath an atmospheric river Partnering with Scripps Institute of Oceanography Will utilize the Manta UAS Instrumentation to include motion sensor and flux package Test flights from Vandenberg AFB in October 2008 with scientific mission in March Dec 07

44 Global Hawk Pacific Storms Mission Enhanced atmospheric profile observations to improve winter storm forecasts on the west coast Potential to combine observations for California water resources and atmospheric river studies Testing targeted for Summer 2009, mission goal of Winter Measurements from dropsondes and, if available, a wind profiling lidar

45 Summary – Part III UAS have been used successfully in many military applications, but they are just starting to be used by civilian agencies, including NOAA. NOAA’s UAS program (http://uas.noaa.gov/) will establish three testbeds: Alaska focusing on global change issues, Southeastern U.S. focusing on Atlantic hurricanes, and Pacific focusing on weather, air quality, marine debris, marine mammal surveys, and fisheries enforcement. NOAA’s Pacific UAS Testbed will test and evaluate Global Hawk and Manta UAS platforms to monitor air-sea interaction and water vapor transport in atmospheric rivers imbedded in Pacific storms. Robbie Hood will become the new UAS project manager for NOAA in a couple of weeks. She will take over for Mart Ralph, who has served extremely well in this capacity for the past few years. Robbie will report directly to Sandy MacDonald, the director of ESRL and the Deputy Assistant Administrator for NOAA Research Laboratories and Cooperative Institutes and co-chair of the NOAA UAS Steering Committee.

46 Russian River Flooding – Monte Rio, CA Thank you!


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