CW3E Publication Notice: High-Elevation Evapotranspiration Estimates During Drought: Using Streamflow and NASA Airborne Snow Observatory SWE Observations to Close the Upper Tuolumne River Basin Water Balance

CW3E Publication Notice

High-Elevation Evapotranspiration Estimates During Drought: Using Streamflow and NASA Airborne Snow Observatory SWE Observations to Close the Upper Tuolumne River Basin Water Balance

March 5, 2018

CW3E postdoc Brian Henn has published a study on estimating evapotranspiration (ET) in California’s Sierra Nevada in Water Resources Research titled High-Elevation Evapotranspiration Estimates During Drought: Using Streamflow and NASA Airborne Snow Observatory SWE Observations to Close the Upper Tuolumne River Basin Water Balance. The study leveraged NASA Airborne Snow Observatory (ASO) and distributed streamflow observations and a basin-scale mass balance approach to estimate ET across the upper Tuolumne River watershed region over three warm seasons (2013-2015), showing spatially coherent totals of about 200 mm per year of ET for these high-elevation areas during California’s recent drought. This represents a novel application of ASO and mass balance approaches to estimate ET at the watershed scale, which is difficult to observe directly. The Tuolumne watershed and others like it the Sierra Nevada are critical water supply areas for California, and changes in ET in the future could impact the reliability of major reservoirs.

The paper was written in collaboration with Tom Painter and Kat Bormann of the NASA ASO team, Bruce McGurk of McGurk Hydrologic, Lorraine and Alan Flint of the USGS, Vince White of Southern California Edison, and Jessica Lundquist of the University of Washington. Please contact Brian at with inquiries.

Figure 1. Figure (1) from Henn et al. (2018): (a) ASO lidar-derived 50 m SWE map for 3 April 2013, over the basin of the Tuolumne River at Highway 120. (b) Example plot for this ASO flight, showing how the basin’s water balance is quantified. All SWE from the 3 April flight is assumed to melt by 30 September ( math formula); cumulative streamflow ( math formula) and precipitation ( math formula) between the flight date and 30 September are then calculated. Uncertainty bounds at 95% confidence are shown for each variable.

Henn, B., Painter, T. H., Bormann, K. J., McGurk, B., Flint, A. L., Flint, L. E., White, V., Lundquist, J. D. (2018). High-elevation evapotranspiration estimates during drought: Using streamflow and NASA airborne snow observatory SWE observations to close the upper tuolumne river basin water balance. Water Resources Research, 54.

CW3E Publication Notice: Global Assessment of Atmospheric River Prediction Skill

CW3E Publication Notice

Global Assessment of Atmospheric River Prediction Skill

February 27, 2018

CW3E collaborators Michael DeFlorio (NASA/JPL), Duane Waliser (NASA/JPL), and Bin Guan (UCLA), along with CW3E director Marty Ralph and colleagues David Lavers and Frederic Vitart of the European Centre for Medium-Range Weather Forecasts (ECMWF), recently published a paper in the Journal of Hydrometeorology titled Global Assessment of Atmospheric River Prediction Skill (early online release; doi:10.1175/JHM-D-17-0135.1). The study introduces the Atmospheric River Skill (ATRISK) algorithm, which is an object-based approach used to quantify atmospheric river (AR) prediction skill using Subseasonal to Seasonal (S2S) Project global hindcast data from ECMWF. Two decades of data from this ensemble hindcast system were used in this work. The ATRISK algorithm determines the distance between the centroids of observed and forecasted ARs (an adjustable parameter; see Fig. 1), which can be used to compute relative operating characteristic (ROC) curves. DeFlorio et. al (2018) shows that climate variability conditions modulate regional AR forecast skill. In particular, over the US West Coast, AR forecast utility (defined as the ratio of hits to false alarms) decreases at 10-day lead during negative Pacific-North America (PNA) conditions, and increases at 10-day lead during positive El Nino and Southern Oscillation (ENSO) conditions, with an even larger increase in AR forecast skill during phase-locked El Niño and positive PNA conditions (Fig. 2).

Figure 1: Figure (2) from DeFlorio et al. (2018): Method of determining if a predicted atmospheric river (AR) is a “hit” or a “miss” relative to an observed AR. Predicted and observed ARs are shown as shaded light and dark shaded ovals, respectively. Their IVT-weighted centroids are shown as black dots, and the distances D1 and D2 between each predicted AR and the observed AR are shown as black arrows. The distance threshold DT, which indicates the acceptable horizontal distance between an observed and predicted AR for a prediction to be considered skillful, is shown as a black arrow. In this example, the prediction of AR1 is considered skillful (a “hit”) since its centroid falls within the distance threshold of the observed AR, while the prediction of AR2 is not considered skillful (a “miss”) since its centroid falls outside the distance threshold of the observed AR.

Figure 2. Figure (10a) from DeFlorio et al. (2018): Relative operating characteristic (ROC) curves composited on positive (red) and negative (blue) phases of the combined El Niño-Southern Oscillation (ENSO) & Pacific-North America teleconnection (PNA) modes in December-January-February (DJF) over the North Pacific/Western U.S. region. The 1000 km distance threshold is used, and positive and negative phases are defined using +/- 0.5 standardized values of the climate index for each mode. 3-day (solid), 7-day (dashed), and 10-day (dotted) lead times are shown. The number of positive and negative phase days for each combined mode phase are listed above the legend. Area under ROC curve distributions for both region/mode/lead times of relevance, calculated from a bootstrap process that was repeated 1000 times by using resampling of the composite positive and negative mode days (red and blue, respectively) and all days (white) distributions with replacement, are included beside the ROC curves.

Deflorio, M., D. Waliser, B. Guan, D. Lavers, F.M. Ralph, and F. Vitart, 2018: Global assessment of atmospheric river prediction skill. Journal of Hydrometeorology, early online release, doi:10.1175/JHM-D-17-0135.1

CW3E Launches Near Real-Time AR and QPF Forecast Verification Website

CW3E Launches Near Real-Time AR and QPF Forecast Verification Website

February 13, 2018

CW3E has developed a new suite of tools designed to quickly evaluate the performance of forecasts for the U.S. West Coast. Tools are now available to verify forecasts of precipitation and integrated vapor transport (IVT), a proxy for atmospheric rivers. The verification method is based upon identifying contiguous regions, called objects, in the forecast that meet the requirements for an impactful precipitation or atmospheric river event. For example, in the below Figure, the forecasted AR (blue shading) is an IVT object (proxy for AR) because it exceeds the threshold 500 kg m-1 s-1 and has geometry consistent with an atmospheric river. The blue shaded forecast IVT object has a location, size and other characteristics that can be compared to an analysis object (blue outline) at the matching forecast valid time. To visualize this process, see the overlap of the two objects in the Figure’s middle panel. On the CW3E website, users may choose to examine precipitation and IVT fields for the previous week; choose one of several forecast sources – including several well-known numerical models; choose a range of forecast lead times; and choose one of several object thresholds. For IVT objects that exceed 500 kg m-1 s-1, additional statistics are provided. The tools are designed to inform operational stakeholders of model performance for key precipitation events and atmospheric river landfalls.

The new verification website is created using NCARs Method for Object-Based Diagnostic Evaluation (MODE) software. These tools were developed by Laurel DeHaan, Andrew Martin, Rachel Weihs, Brian Kawzenuk and Chad Hecht of CW3E and David Reynolds of CIRES. They can be accessed from the CW3E forecast verification webpage. A more complete explanation of the verification and the methodology is provided on the website.

Additional forecast verification tools are in development at CW3E and will be posted to the website as they become available.

(Left) Forecasted IVT from the NCEP GFS, initialized 1200 UTC 24 January 2018 and valid 1200 UTC 29 January 2018.
(Middle) Forecasted (shading) and observed (contour) IVT objects identified by MODE using a 500 kg m-1 s-1 threshold.
(Right) GFS analysis (0-hr forecast) IVT valid 1200 UTC 29 January 2018.

CW3E Publication Notice: Genesis, Pathways, and Terminations of Intense Global Water Vapor Transport in Association with Large-Scale Climate Patterns

CW3E Publication Notice

Genesis, Pathways, and Terminations of Intense Global Water Vapor Transport in Association with Large-Scale Climate Patterns

February 13, 2018

CW3E researchers Scott Sellars and Brian Kawzenuk and director Marty Ralph in collaboration with Phu Nguyen (UC Irvine) and Soroosh Sorooshian recently published a paper in Geophysical Research Letters titled Genesis, Pathways, and Terminations of Intense Global Water Vapor Transport in Association with Large-Scale Climate Patterns ( The study uses the CONNected objECT (CONNECT) algorithm applied to integrated water vapor transport (IVT) data for the period of 1980 to 2016 calculated from Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) to identify objects associated with extreme moisture transport (Sellars et al., 2013, 2015).

The algorithm generated a global dataset of life-cycle records in time and space of evolving strong water vapor transport events. Each object was associated with distinct physical and climatological features such as object size, location, and intensity, various climatological teleconnection patterns, and many other characteristics. This algorithm identified various weather phenomena associated with strong moisture transport such as atmospheric rivers, hurricanes and tropical cyclones, monsoon transport, and various other systems that produced extreme moisture transport. It was illustrated that these events typically occurred in five distinct regions located in the midlatitudes (off the coast of the southeast United States, eastern China, eastern South America, off the southern tip of South Africa, and in the southeastern Pacific Ocean) (Figure 1a). Additional analysis showed distinct genesis and termination regions and global seasonal peak frequency during Northern Hemisphere late fall/winter and Southern Hemisphere winter (Figure 1c and d). In addition, the frequency and location of these events were shown to be strongly modulated by the Arctic Oscillation, Pacific North American Pattern, and the Quasi-Biennial Oscillation. Moreover, a positive linear trend in the annual number of objects was reported, increasing by 3.58 objects year-over-year. The vast dataset produced in this study will be used for various future research opportunities focused on extreme moisture transport and its connection to large-scale climate dynamics.

Figure 1:(a) Total number of IVT objects from January 1980 to August 2016. (b) Average duration in hours of object at each grid cell. (c) The number of objects at genesis (starting) locations for all IVT objects. (d) The number of objects at termination (ending) locations for all IVT objects. The gray areas represent landmass.

Atmospheric River Reconnaissance – 2018 is Underway

Atmospheric River Reconnaissance – 2018 is Underway

February 8, 2018

Beginning on January 19th with a dry run for forecast and flight planning operations, CW3E director Marty Ralph has been leading the Atmospheric River Reconnaissance (AR Recon; 2018 campaign, in close collaboration with Co-PI Vijay Tallapragada of the National Center for Environmental Prediction (NCEP) and Jim Doyle of the Naval Research Laboratory (NRL). AR Recon 2018 supports improved prediction of landfalling atmospheric rivers on the US west coast, which is a type of storm that is key to the region’s precipitation, flooding and water supply (e.g., Ralph et al. 2012, 2013, 2016; Dettinger et al. 2011; Neiman et al. 2011). Forecasts of landfalling ARs are key to precipitation prediction and yet are in error by +/- 400 km at even just 3-days lead time (Wick et al., 2013). The concept for AR Recon was first recommended in a report to the Western States Water Council that was prepared by a broad cross-disciplinary group in 2013 (Ralph et al. 2014).

Key sponsors are the U.S. Army Corps of Engineers and the CA Department of Water Resources, who are working with CW3E and other partners to advance their goals of using improved AR prediction to inform water and infrastructure management (e.g. FIRO, CW3E AR Monitoring, Analysis and Prediction System). This campaign has been conducted with participation of experts on midlatitude dynamics, atmospheric rivers, airborne reconnaissance, and numerical modeling, who have come together from organizations including CW3E at UC San Diego’s Scripps Institution of Oceanography, NOAA (NWS’ NCEP and Western Region, OMAO/Aircraft Operations Center), NRL, the Air Force 53rd Weather Reconnaissance Squadron, Plymouth State Univ., the National Center for Atmospheric Research, U Albany, U Arizona, and the European Centre for Medium-Range Weather Forecasts (ECMWF), and have participated in daily forecasting and flight planning discussions. The team and its efforts for the first Intensive Observing Period (IOP) are summarized in Fig. 1.

Fig. 1. Summary of planning team and plans for the first IOP of AR Recon – 2018.

Fig. 2. Dropsondes on the NOAA G-IV aircraft before getting released through the chute (below left). Each dropsonde is about the size of two soda cans. Photo courtesy Dr. Brian Henn.

Three aircraft that are normally used for hurricane reconnaissance are being deployed for atmospheric river reconnaissance this winter. The data are being incorporated by global modeling centers. The flights are conducted over the Northeast Pacific to collect observations to support improved AR forecasts. These aircraft include two of the Air Force 53rd Weather Reconnaissance Squadron’s WC-130J Hurricane Hunter aircraft, one based in Hawaii and the other in California, and NOAA’s Gulfstream IV (G-IV), based in Everett, WA. Air Force personnel have been stationed at Scripps to help coordinate flight planning. The primary data collected are from the release of dropsondes (Fig. 2), which record temperature, wind, and relative humidity at very high resolution throughout the atmosphere. Dr. Jennifer Haase and postdocs Michael Murphy and Bing Cao flew additional instrumentation aboard the NOAA G-IV to characterize the upper atmosphere poleward of the atmospheric rivers. A simplified version of the GNSS Instrument System for Multistatic and Occultation Sensing (GISMOS) was used to measure profiles of the atmospheric environment to the sides of the aircraft while dropsondes measure profiles directly below the aircraft. The data will be analyzed after the campaign to investigate whether temperature variations not captured in the numerical weather models had an impact on storm development.

To date, four missions have been flown (Fig. 3), and a total of 306 successful dropsonde releases were completed. Three of these missions included all three aircraft, and one included the two C-130s without the G-IV. Flights were all centered at 0000Z, with drops occurring in the +/- 3 hour time window, so that the data the aircraft gathered could be assimilated into operational numerical weather prediction models, including NWS’ Global Forecast System (GFS), ECMWF, and NRL’s Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS).

Fig. 3. Flight tracks and actual drop locations for all aircraft over GFS analysis IVT (integrated vapor transport) at drop center time. Figure provided by Brian Kawzenuk.

ARRecon-2018 is making use of an exciting tool developed by the NRL (Doyle et al., 2012), a moist adjoint model that pinpoints location of greatest sensitivity in the forecast – which is centered typically on the atmospheric river core. This moisture sensitivity is substantially larger than temperature, wind, or any other sensitivity. The field campaign is refining how the tool works, and that information is being combined with knowledge of dynamically significant meteorological features such as the upper-level jet, cold-air troughs and other features to specify each mission’s detailed flight tracks. These inputs were developed through group discussions such as is pictured (Fig. 4) on 25 January 2018. The dropsonde data collected in these targeted locations in the otherwise data sparse ocean may be part of the solution to getting atmospheric river forecasts right. Analysis of the impact of the data will be carried out over the next couple of years to thoroughly assess this, including development of specialized assimilation methods. Not only will these data be assimilated into operational forecast models, but the information collected will be used in research studies to further understand the dynamics and processes that are the main drivers of key atmospheric river characteristics such as strength, position, length, orientation, and duration.

Fig. 4. Daily flight planning meeting on 25 January 2018 at Scripps/CW3E. Clockwise from left front: F. Martin Ralph (PI/Mission Director), Maj. Ashley Lundry (US Air Force, C-130 Flight Director), Grant Wagner (US Air Force Navigator), Matt Hawcroft (Observer), Jay Cordeira (Plymouth St. Univ., Forecasting Lead), Chad Hecht (Staff Researcher CW3E, Forecaster), Forest Cannon (CW3E PostDoc, Flight Planning Coordinator), Aneesh Subramanian (CW3E Project Scientist, Modeling and Data Assimilation Lead). Participants, but not in photo, Jim Doyle (NRL, AR Recon Alternate Mission Director), Anna Wilson (CW3E Field Research Manager, AR Recon Coordinator), Jon Rutz (NWS, C-130 Flight Planning Lead), Chris Davis (NCAR, G-IV Planning Lead), Carolyn Reynolds (NRL, Moist Adjoint Team Lead), Tom Galarneau (Univ. of Arizona, G-IV Flight Planning Support), Reuben Demirdjian (CW3E grad student, Adjoint team), Lance Bosart (SUNY Albany, Flight planning input).

Photos from IOP 4 illustrate the experience of a flight on the NOAA G-IV (Fig. 5).

Fig. 5. Photos from the NOAA G-IV IOP-4 flight on 3 Feb 2018 from Paine Field in Everett, WA (near Seattle). Flight lasted 8.2 hours, covered 3400 nautical miles and released 39 dropsondes. Clockwise from top left: Scientists and crew head out to board the G-IV; Tanner Sims (AOC, Pilot) in the cockpit; F. Martin Ralph (CW3E/Scripps, PI) holding a dropsonde; a view of the G-IV’s wing in flight; David Cowan (AOC, Pilot) and Richard Henning (AOC, Flight Director); Anna Wilson (CW3E/Scripps; AR Recon Coordinator) and Jessica Williams (AOC, Flight Director). Photos courtesy F. Martin Ralph.

While the NOAA G-IV has completed its mission in the west for this year, there are still two more storms to be sampled by the C-130s through the end of February 2018. Stay tuned for more information on those missions!

For more information on the AR Recon 2018 campaign, please see the following stories:


Dettinger, M.D., Ralph, F.M., Das, T., Neiman, P.J., and Cayan, D., 2011: Atmospheric rivers, floods, and the water resources of California. Water, 3 (Special Issue on Managing Water Resources and Development in a Changing Climate), 455-478.

Doyle, J.D., C.A. Reynolds, C. Amerault, and J. Moskaitis, 2012: Adjoint sensitivity and predictability of tropical cyclogenesis. J. Atmos. Sci., 69, 3535-3557.

Neiman, P. J., L. J. Schick, F. M. Ralph, M. Hughes, G. A. Wick, 2011: Flooding in Western Washington: The Connection to Atmospheric Rivers. J. Hydrometeor., 12, 1337-1358, doi: 10.1175/2011JHM1358.1.

Ralph, F. M., and M. D. Dettinger, 2012: Historical and national perspectives on extreme West Coast precipitation associated with atmospheric rivers during December 2010. Bull. Amer. Meteor. Soc., 93, 783-790.

Ralph, F. M., T. Coleman, P.J. Neiman, R. Zamora, and M.D. Dettinger, 2013: Observed impacts of duration and seasonality of atmospheric-river landfalls on soil moisture and runoff in coastal northern California. J. Hydrometeor., 14, 443-459.

Ralph, F. M., M. Dettinger, A. White, D. Reynolds, D. Cayan, T. Schneider, R. Cifelli, K. Redmond, M. Anderson, F. Gherke, J. Jones, K. Mahoney, L. Johnson, S. Gutman, V. Chandrasekar, J. Lundquist, N.P. Molotch, L. Brekke, R. Pulwarty, J. Horel, L. Schick, A. Edman, P. Mote, J. Abatzoglou, R. Pierce and G. Wick, 2014: A vision for future observations for Western U.S. extreme precipitation and flooding– Special Issue of J. Contemporary Water Resources Research and Education, Universities Council for Water Resources, Issue 153, pp. 16-32.

Ralph, F. M., J. M. Cordeira, P. J. Neiman and M. Hughes, 2016: Landfalling atmospheric rivers, the Sierra barrier jet and extreme daily precipitation in northern California’s upper Sacramento river watershed. J. Hydrometeor., 17, 1905-1914.

Wick, G.A., P.J. Neiman, F.M. Ralph, and T.M. Hamill, 2013: Evaluation of forecasts of the water vapor signature of atmospheric rivers in operational numerical weather prediction models. Wea. Forecasting, 28, 1337-1352.

CW3E Graduate Student Attends the RIKEN International School on Data Assimilation Workshop

CW3E Represented at the National Council for Science and the Environment National Conference and Global Forum

February 2, 2018

Third year PhD student, Meredith Fish, was accepted to attend the RIKEN International School on Data Assimilation for a workshop in 2018 in Kobe, Japan on full scholarship. RIKEN is Japan’s largest research institute and houses its K computer, ranked number one for 5 consecutive years on the Graph500 supercomputer ranking. Research at the Advanced Institute for Computational Science at RIKEN focuses on weather forecasting, earthquake/tsunami forecasting, space science, drug discovery and manufacturing development.

During the workshop students attended lectures and participated in hands-on sessions lead by top leaders in data assimilation such as Stephen Penny (NCEP), Eugenia Kalnay (U. of Maryand), Gregory Hakim (U. Washington), Sebastian Reich (U. Reading) and Henry Abarbanel (UCSD). Lecture topics included the mathematical background of data assimilation, geoscience applications, data assimilation within a toy model, machine learning and coupled human-earth systems.

This workshop has prepared Meredith to use data assimilation in her research and actively participate in the AR Recon field campaign, which is attempting to leverage data assimilation techniques to quantify uncertainties in forecasts using targeted observations.

Meredith standing in from of the Advanced Institute for Computational Sciences at RIKEN in Kobe, Japan.

CW3E Publication Notice: An Inter-comparison Between Reanalysis and Dropsonde Observations of the Total Water Vapor Transport in Individual Atmospheric Rivers

CW3E Publication Notice

An Inter-comparison Between Reanalysis and Dropsonde Observations of the Total Water Vapor Transport in Individual Atmospheric Rivers

February 2, 2018

CW3E collaborators Bin Guan (UCLA), Duane Waliser (NASA/JPL), along with CW3E director Marty Ralph, recently published a paper in the Journal of Hydrometeorology, titled An Inter-comparison Between Reanalysis and Dropsonde Observations of the Total Water Vapor Transport in Individual Atmospheric Rivers ( The paper is included in the journal’s special collection on MERRA-2, Modern-Era Retrospective analysis for Research and Applications version 2.

Using airborne observations from various field campaigns over the northeastern Pacific along with two atmospheric reanalysis products (ERA-Interim and MERRA-2), the study validated key characteristics of atmospheric rivers (ARs) depicted by reanalyses against observations, as well as evaluating how well the 21 observed ARs represent the total of about 6000 ARs that occurred during the winters of 1979-2016 over the northeastern Pacific.

Results showed that the reanalysis products accurately depict the strength of the observed ARs in terms of the total water vapor flowing along an individual AR across its entire width, with a mean error of only +3% or -1% depending on the reanalysis product being evaluated. Additionally the 21 observed ARs well represent the mean strength of the total of about 6000 ARs identified in reanalysis products, with a mean difference of 5% or 14% depending on the reanalysis product being compared. Similar comparisons were also done for AR width, and for ARs in other regions and seasons. The study highlights the values of both dedicated observations of specific cases and spatiotemporally more complete global reanalysis products in understanding the characteristics and impacts of ARs.

Figure Caption: (left) Histogram of AR widths based on all ARs detected in ERA-Interim over the northeastern Pacific (AR centroids within 163.4–124.6°W, 23–46.4°N) during 15 January to 25 March of 1979–2016 (gray bars). Also shown are the mean AR width (km) based on all reanalysis ARs that contributed to the histogram (red solid), the subset of the reanalysis ARs that correspond to the 21 dropsonde transects (red dashed), and the observed value based on the 21 dropsonde transects as reported in Ralph et al. (2017b) (blue dashed for the mean, and blue circles for individual transects). The mean AR width value is also indicated in the figure legend for each sample. Red shading indicates the 95% confidence interval of the mean reanalysis AR width for a random 21-member sample drawn from the pool of reanalysis ARs based on 10,000 iterations. The error bar centered on the blue dashed line indicates the 95% confidence interval of the difference between the blue and red dashed lines based on a two-tailed, paired t-test. (right) As in the left but for total integrated water vapor transport (108 kg s−1) across AR widths.

CW3E Launches Interactive AR Rain Versus Snow Forecast Maps and Watershed Plots

CW3E Launches Interactive AR Rain Versus Snow Forecast Maps and Watershed Plots

January 26, 2018

CW3E has launched a new forecast tool designed to visualize the impacts of the freezing level (and thus the rain versus snow partitioning in mountain watersheds) during atmospheric river storms over the U.S. West Coast. The interactive map shows whether the NOAA NCEP global forecasting ensemble predicts rain, snow, or uncertainty (some models predicting rain, some snow) downscaled to a 1 km resolution, and out to 7 days forecast lead times. For major watersheds in California, Oregon, Washington, Idaho, and Nevada time series of the forecasted freezing levels, precipitation amounts, and fractions falling as rain versus snow are also availalbe. The tool is designed to be of used for operational stakeholders for regions that are sensitive to the impacts of precipitation phase on hydrology, infrastructure, and public safety. This tool was developed by Jason Cordeira (Plymouth State University) and Brian Henn (CW3E) and is available on the CW3E Interactive Maps webpage.

CW3E Fieldwork Season Begins

CW3E Fieldwork Season Begins

January 10, 2018

A team of CW3E postdocs, students, staff, and collaborators headed to Northern California on Sunday, 7 January to begin the winter 2018 fieldwork campaign. Throughout this winter season, CW3E plans to release radiosondes, conduct stream surveys, and collect isotope samples. The campaign aims to continue efforts in understanding atmospheric rivers (ARs) and their impacts on the Russian River Watershed. In support of the Forecast Informed Reservoir Operation (FIRO), hydrometeorological data from the campaign will be used to enhance water resources and flood control operations.

The team is launching from two sites: a coastal site, the UC Davis Bodega Bay Marine Laboratory and an inland site in Ukiah, CA, southwest of Lake Mendocino. These launches are being shared with National Weather Service Weather Forecast Offices in Eureka, Sacramento, and Monterey. Peak launches recorded 511 units integrated water vapor transport (IVT) at Bodega Bay (0000Z 9 January 2018) and 389 units IVT at Ukiah (2100Z 8 January 2018).

A radiosonde launch completed in Bodega Bay (0259Z 9 January 2018) shows a sounding with typical AR conditions.

Note: no orographic enhancement present (NOAA Earth System Research Laboratory)

The Regional and Mesoscale Meteorology Branch (RAMMB) of NOAA/NESDIS and Cooperative Institute for Research in the Atmosphere (CIRCA)

Leah Campbell and Anna Wilson, Postdocs, prepare to release radiosondes from Bodega Bay

Photograph taken at the mouth of the Russian River after the storm.

Other members of the team have been working on stream installations and measurements, along with isotope sampling. Working with the Sonoma County Water Agency (SCWA), CW3E has begun inventorying supplies to continue using the stream gauges that were installed during the previous fieldwork season. They have completed discharge measurements at five of the six streams where gauges are deployed, and will complete measurements at the remaining site today.

The team will continue collecting data and releasing radiosondes throughout this event with plans to return to sample ARs as they occur in the coming months. CW3E will also be partnering with NOAA and the U.S. Air Force, as part of the field campaign, for a series of Reconnaissance (Recon) flights into AR events. The AR Recon missions will start on 25 January, and continue through 28 February. In addition to the NOAA G-IV aircraft, flying out of Seattle for three storms, the campaign will also include two Air Force C-130s that will fly through a total of six storms, overlapping with the NOAA G-IV for three storms. These flights are a valuable method in improving the forecasting of AR conditions offshore and can provide enhanced prediction of AR landfall duration and intensity.

Odds of Reaching 100% Water Year Precipitation – Jan Update

Odds of Reaching 100% of Normal Precipitation for Water Year 2018 (January Update)

January 8, 2018

Contribution from Dr. M.D. Dettinger, USGS

The odds shown here are the odds of precipitation in the rest of the water year (after December 2017) totaling a large enough amount to bring the water-year total to equal or exceed the percentage of normal listed. “All Yrs” odds based on monthly divisional precipitation totals from water year 1896-2015. Numbers in parenthesis are the corresponding odds if precipitation through December had been precisely normal (1981-2010 baseline).

Click here for a pdf file of this information.




How these probabilities were estimated:

At the end of a given month, if we know how much precipitation has fallen to date (in the water year), the amount of precipitation that will be required to close out the water year (on Sept 30) with a water-year total equal to the long-term normal is just that normal amount minus the amount received to date. Thus the odds of reaching normal by the end of the water year are just the odds of precipitation during the remaining of the year equaling or exceeding that remaining amount.

To arrive at the probabilities shown, the precipitation totals for the remaining months of the water year were tabulated in the long-term historical record and the number of years in which that precipitation total equaled or exceeded the amount still needed to reach normal were counted. The fraction of years that at least reached that threshold is the probability estimate. This simple calculation was performed for a full range of possible starting months (from November thru September) and for a wide range of initial (year-to-date) precipitation conditions. The calculation was also made for the probabilities of reaching 75% of normal by end of water year, 125%, and 150%, to ensure that the resulting tables of probabilities cover almost the full range of situations that will come up in the future.

[One key simplifying assumption goes into estimating the probabilities this way: The assumption that the amount of precipitation that will fall in the remainder of a water year does not depend on the amount that has already fallen in that water year to date. This assumption was tested for each month of the year by correlating historical year-to-date amounts with the remainder-of-the-year amounts, and the resulting correlations were never statistically significantly different from zero, except possibly when the beginning month is March, for which there is a small positive correlation between Oct-Mar and Apr-Sept precipitation historically.]

Contact: Michael Dettinger (USGS)