Author: cw3e-web

CW3E Publication Notice: PERSIANN Dynamic Infrared–Rain Rate Model (PDIR) for High-Resolution, Real-Time Satellite Precipitation Estimation

CW3E Publication Notice

PERSIANN Dynamic Infrared–Rain Rate Model (PDIR) for High-Resolution, Real-Time Satellite Precipitation Estimation

April 23, 2020

A team from the Center for Hydrometeorology and Remote Sensing (CHRS) at UC Irvine led by Dr. Phu Nguyen, along with CW3E director Dr. Marty Ralph, recently published a paper entitled “PERSIANN Dynamic Infrared–Rain Rate Model (PDIR) for High-Resolution, Real-Time Satellite Precipitation Estimation” in the Bulletin of the American Meteorological Society (BAMS) where they introduce a new real-time satellite quantitative precipitation estimation technique – PDIR model. PDIR was created as part of the Atmospheric River (AR) program, a multi-organizational, multi-discipline program focused on becoming more resilient to extreme precipitation. A key strategy of the AR Program is to identify and fill observational data gaps to better prepare for extreme precipitation events, particularly ARs. As part of the AR Program’s observational strategy, the PDIR technique improves upon the current state of real-time satellite retrievals of precipitation totals over the US West Coast, which are imperative tools for natural hazard agencies combating AR-related flooding.

PDIR is based on the framework of the PERSIANN-Cloud Classification System (CCS) algorithm, which is in turn based on the original PERSIANN framework. PERSIANN, PERSIANN-CCS, and PDIR are satellite-based data-driven precipitation estimating systems that work primarily by establishing a link between geostationary-derived cloud-top temperature from infrared and rain rate using machine learning techniques. PDIR was created for the purpose of developing a high-spatiotemporal-resolution, near-real-time precipitation dataset that solely utilizes infrared data as input. PDIR advances the framework of the PERSIANN-CCS system by improving the capture of warm precipitation by adapting higher temperature thresholds, expanding the cloud classification system to include monthly sets of cloud types, and utilizing gridded monthly rainfall climatology data to create a dynamically shifting cloud top temperature–rain rate curve relationship.

PDIR was shown to accurately mimic the observed spatial patterns of rainfall over the western Contiguous U.S. (CONUS), notably the high rainfall amounts over the Cascade Range and Sierra Nevada. PDIR’s noteworthy performance in capturing the western CONUS’s rainfall, especially with high intensities and over mountainous regions, suggests a level of success in adapting to the challenges of the differing rainfall regimes that are intrinsic to the area, i.e. brightband, nonbrightband, and hybrid (seeder-feeder) rainfall. Of particular interest is that PDIR yields rainfall estimates for extreme rainfall events like ARs that are comparable to the nearly instantaneous, multi-sensor product, Stage II. Furthermore, the spatial patterns of PDIR’s precipitation estimates in mountainous regions better mimic those recorded by Stage IV than those shown by Stage II. The algorithm’s success at these scales suggests that PDIR contains the spatiotemporal richness and near-instantaneous availability necessary for rapid hazard response while showing potential to be useful for hydrologic and water resources applications, of which the latter has been a major weakness of infrared-based algorithms to date. As the implications of this study are valuable to a global audience, PDIR’s future direction is chiefly focused on its global implementation.

Figure 1. The dynamic cloud-top brightness temperature (Tb)-rain rate (RR) model: In PERSIANN-CCS, clouds that are identical in shape, size, temperature distribution, and all other defining characteristics but spatial location have completely identical RR readings. However, in PDIR, the underlying climatology causes shifts in the Tb-RR curves that characterize cloud-based models like PERSIANN-CCS and PDIR. In PDIR, drier climatologies such as the Mojave Desert (MD) cause a leftward shift in the Tb-RR relationships, which results in lower precipitation rates than at identical temperatures in moderately wet regions such as California’s Central Coast (CC), while wetter climatologies such as the Klamath National Forest (KNF) cause a rightward shift, which causes an increase in estimated precipitation rates for identical temperatures.

Nguyen, P., E. J. Shearer, M. Ombadi, V. A. Gorooh, K. Hsu, S. Sorooshian, W. S. Logan, and M. Ralph, 2020: PERSIANN Dynamic Infrared–Rain Rate Model (PDIR) for High-Resolution, Real-Time Satellite Precipitation Estimation. Bull. Amer. Meteor. Soc., 101, E286-E302, https://doi.org/10.1175/BAMS-D-19-0118.1

CW3E Event Summary: 5-10 April 2020

CW3E Event Summary: 5-10 April 2020

April 15, 2020

Click here for a pdf of this information.

Unsettled weather pattern brings record precipitation to Southern California

  • Multiple episodes of heavy rainfall during 5–10 Apr were associated with a cutoff low near the U.S. West Coast
  • More than 2 inches of precipitation fell over a large portion of Southern California, with the highest amounts (> 6 inches) in the Transverse Ranges and northern San Diego County
  • Significant snowfall (> 12 inches) occurred in the higher elevations of the Transverse Ranges
  • Intense rainfall resulted in flash flooding throughout San Diego County on 10 Apr

Click 500-hPa Absolute Vorticity or IWV image to see loop of GFS analyses

Valid 0000 UTC 5 April – 1200 UTC 11 April 2020


 

 

 

 

 

 

Summary provided by C. Castellano, C. Hecht, J. Kalansky, B. Kawzenuk, and F. M. Ralph; 15 April 2020

CW3E Publication Notice: Quantifying the Spatial Variability of a Snowstorm Using Differential Airborne Lidar

CW3E Publication Notice

Quantifying the Spatial Variability of a Snowstorm Using Differential Airborne Lidar

April 13, 2020

CW3E post-doc, Ty Brandt, lead a recently published paper presenting a new methodology to measure snowfall and compared to other SWE estimates. Accurate measures of snowfall are important because California depends on snow accumulation in the Sierra Nevada for its water supply, and accurate estimates of the total water stored in the snowpack are essential to water resource management. Presently, snowfall is measured by a combination of snow pillows, snow courses, and rain gauges. However, the scarcity of these measurements, particularly at high elevations, and their associated errors impose a limit on hydrologic forecasting. To reduce forecast errors, we require high-resolution, spatially-complete measurements of precipitation amount and phase. In snow-dominated watersheds, remotely sensed snow depth and snow water equivalent (SWE), with retrieval time scales that resolve individual storms, may ameliorate the existing data gap. Co-authors on the paper included CW3E’s Forest Cannon and Daniel Steinhoff, as well as collaborators from U.C. Santa Barbara, NASA Jet Propulsion Laboratory, the National Snow and Ice Data Center, and UCLA.

Since 2013, NASA’s Airborne Snow Observatory (ASO) has used airborne lidar to accurately measure snow depth in California’s Sierra Nevada for the purposes of advancing streamflow forecasting. However, in early April 2015, two flights 6 days apart happened to bracket a single snow storm. The research utilized these flights to develop a methodology using airborne lidar to directly measure the spatial variability of accumulated snow as a proxy for “snowfall” at a 50 m spatial resolution. In an end-to-end analysis, the researchers also compared gauge-interpolated and dynamically downscaled estimates of precipitation for the given storm with that of the ASO accumulated SWE (see figure below). This particular event proved an interesting test case due to the storm’s moderate snowfall generated by high amounts of dynamical forcing rather than strong orographic enhancement. Results highlighted that statistically-interpolated precipitation estimates were overly-reliant on climatological patterns of orographic enhancement, and that although ensemble WRF simulations accurately represented the event’s evolution and precipitation mechanisms, their snowfall distributions were highly sensitive to model parameterization choices.

While the April 2015 storm was a peculiar end-member for storms in the Sierra Nevada (quite distinct from the more impactful atmospheric rivers and their predominant orographic influences) it did serve as an important example to demonstrate the potential of differential lidar observations to quantify snowfall. By directly observing the snowfall distribution, ASO data provided validation of the hydrometeorology across complex terrain, in a way that has never before been possible. Research that utilizes this technology to further investigate multiple storms, spanning a variety of snow climates, would enhance our understanding of precipitation mechanisms. Ultimately, this would improve mountain precipitation representation within statistical methods, weather models, and climate models, directly helping to improve the accuracy and precision with which we can forecast hydrometeorological extremes.

Comparison between the ASO derived accumulated snow and 10 different precipitation products including two gauge-interpolation products and eight different WRF simulations using six different microphysics and three different PBL schemes. (a-j) Spatial maps of the difference between each of the precipitation estimates and ASO; all WRF products used the RAINNC output which is akin to total precipitation. (k) total water equivalent over the Tuolumne as measured by ASO and all ten precipitation products. All WRF simulations have a “snow” and “precipitation” bar because WRF can parse liquid and frozen precipitation. The difference between the WRF precipitation and snow estimates is rain.

Brandt, W. T., Bormann, K. J., Cannon, F., Deems, J. S., Painter, T. H., Steinhoff, D. F., & Dozier, J. (2020) Quantifying the spatial variability of a snowstorm using differential airborne lidar. Water Resources Research, 56, e2019WR025331. https://doi.org/10.1029/2019WR025331

CW3E Welcomes Ellen Knappe_Postdoc

CW3E Welcomes Ellen Knappe

April 13, 2020

Ellen Knappe joined CW3E in February 2020 as a joint postdoc with IGPP. El finished her PhD in 2019 at the University of Montana working on constraining earth deformation using geodetic time series with Dr. Rebecca Bendick. Her research included quantifying seasonal deformation due to hydrologic loading in the mountainous watersheds in the northern Rockies. Prior to coming to Scripps Institution of Oceanography (SIO), she received her BA in Geophysics from UC Berkeley (2014) and completed a short postdoc at UM with Dr. Bendick and Dr. Payton Gardner (2020).

El’s research at UM included using geodetic observations as an independent measure of hydrologic storage in mountainous watersheds. With dense networks of stations across the US and daily observations, geodetic time series can constrain water mass at high temporal and spatial resolution. At SIO, working under advisor Adrian Borsa, she will apply and expand these methods to other regions, including the Pacific Northwest and California, working towards quantifying water storage in mountainous watersheds, water flux due to large precipitation events and tracking water through watersheds with time. El looks forward to working with CW3E to integrate geodetic observations into existing hydrologic and watershed models.

CW3E Welcomes Wen-Shu Lin

CW3E Welcomes Wen-Shu Lin

April 9, 2020

Wen-Shu Lin joined CW3E as a graduate student in the fall of 2019 after receiving her B.S. from National Taiwan University with a major in Atmospheric Science. During her undergraduate research she investigated the connections between large-scale sea surface temperatures, precipitation, and atmospheric circulations under varying stages of global warming. Her results illustrated that while there are some known patterns of future warming, regional variability among climate models is large, and at times, provides outputs of varying sign and direction. These discrepancies among climate models has sparked her interest in regional climate and she will look to continue that work at SIO and CW3E.

At CW3E, under the direction of co-advisors Joel Norris and F. Martin Ralph, she will continue her research into climate variabilities, coupled atmosphere-ocean interactions, and atmospheric dynamics. Her research will focus on closing the gap between weather and climate, specifically focused on the predictability of atmospheric rivers and regional climate on subseasonal-to-seasonal time scales.

Distribution of Landfalling Atmospheric Rivers over the U.S. West Coast during Water Year 2020: October through March Update

Distribution of Landfalling Atmospheric Rivers over the U.S. West Coast during Water Year 2020: October through March Update

April 8, 2020

For a pdf of this information click here.
 

 

 

More information on water year precipitation and odd’s of reaching normal accumulations can be found here
 

 

 

 

Analysis by Chad Hecht & F. Martin Ralph. This analysis is considered experimental. For questions regarding the data or methodology please contact Chad Hecht

CW3E AR Update: 7 April 2020 Summary

CW3E AR Update: 7 April 2020 Summary

April 7, 2020

Click here for a pdf of this information.

A weak but seasonally anomalous atmospheric river brought precipitation to a large portion of California

  • Numerous coastal locations experienced IVT magnitudes >250 kg m–1 s–1 for <24 hours during this event
  • This is the third time since 2000 that San Diego has experienced IVT >250 kg m–1 s–1 during an AR in the first week of April
  • Numerous high elevation locations across California received >2 feet of snow in association with this AR
  • Lower elevations across much of the state have received 0.75 to 1.5 inches of liquid precipitation
  • As the large-scale system begins to weaken and propagate inland, it is forecast to bring additional precipitation to portions of Southern California

SSMI/SSMIS/AMSR2-derived Integrated Water Vapor (IWV)

Valid 0000 UTC 3 April – 1800 UTC 7 April 2020

Images from CIMSS/Univ. of Wisconsin

Click IVT or IWV image to see loop of GFS Analysis

Valid 1200 UTC 3 April – 1200 UTC 7 April 2020

 

 

 

 

 

 

Summary provided by C. Hecht, C. Castellano, Z. Zhang, J. Kalansky, and F. M. Ralph; 4 PM PT 7 April 2020

CW3E AR Update: 3 April 2020 Outlook

CW3E AR Update: 3 April 2020 Outlook

April 3, 2020

Click here for a pdf of this information.

An upper-level trough and a landfalling AR will bring rainfall and mountain snowfall to California

  • An amplifying upper-level trough will form a closed low as it slowly moves along the U.S. West Coast
  • A weak AR is forecast to develop south of the trough and bring AR conditions to Central and Southern California
  • Moderate rainfall (0.5–2 inches) is expected at lower elevations, with higher amounts (2–4 inches) in the Northern California Coast Ranges, Klamath Mountains, and Southern California Transverse Ranges
  • The heaviest precipitation (3–5 inches) is expected over the Sierra Nevada, with 2–4 feet of snow possible in some areas

Click IVT or IWV image to see loop of GFS analyses/forecasts

Valid 0000 UTC 4 April – 0000 UTC 8 April 2020


 

 

 

 

 

 

 

 

Summary provided by C. Castellano, C. Hecht, and F. M. Ralph; 3 April 2020

*Outlook products are considered experimental

CW3E Publication Notice: Detection Uncertainty Matters for Understanding Atmospheric Rivers

CW3E Publication Notice

Detection Uncertainty Matters for Understanding Atmospheric Rivers

April 2, 2020

Indiana University assistant professor Dr. Travis O’Brien and co-authors recently published an article in the Bulletin of the American Meteorological Society (BAMS) titled “Detection Uncertainty Matters for Understanding Atmospheric Rivers”. Several members of CW3E contributed as co-authors to this work, including Christopher Castellano, Mike DeFlorio, Brian Kawzenuk, Allison Michaelis, and Zhenhai Zhang. This paper builds upon earlier atmospheric river detection tools (ARDT) work at CW3E: i.e., some of the ARDTs studied in ARTMIP were developed by CW3E scientists (Rutz et al. 2014; Mon. Wea. Rev. and Gershunov et al. 2017; Geophys. Res. Lett.), and CW3E scientists led or contributed to recent work comparing ARDTs [Shields et al. (2018; Geosci Model Dev.), Rutz et al. (2019; JGR-A) and Ralph et al. (2019; Clim. Dynam.) – see /publications/ for links to these papers].

The purpose of this BAMS article is to summarize the 3rd ARTMIP (Atmospheric River Tracking Method Intercomparison Project) Workshop (held during October 2019 at Lawrence Berkeley National Laboratory [LBL]), and to highlight current and emerging scientific endeavors related to atmospheric river (AR) detection and tracking. Many CW3E research and product development efforts rely on accurate and robust detection of ARs in order to meet the needs of our stakeholders at both the California Department of Water Resources and U.S. Army Corps of Engineers.

The ARTMIP project was created in order to facilitate comparisons between AR detection and tracking schemes across different research groups. Quantifying the uncertainty associated with AR detection and tracking methods is vital for interpreting observational and modeling studies within the AR research and experimental forecast product development community. For more information on ARTMIP visit http://www.cgd.ucar.edu/projects/artmip/, this site also includes a list of publications generated by and associated with ARTMIP, available here.

The 1st ARTMIP Workshop was held in May 2017 at CW3E, and the 2nd ARTMIP Workshop was held in April 2018 in Gaithersburg, MD. The 3rd ARTMIP Workshop contained presentations from researchers across the AR community, and also included an interactive session where researchers hand-identified AR objects using LBL’s ClimateNet machine learning software. A comparison between expert AR identifications is shown in Figure 1, along with the background integrated water vapor field for that day.

The major outcomes of the workshop were to expand the research topics to be included in the ARTMIP project going forward, including reanalysis sensitivity studies and paleoclimate studies, and to continue to provide the AR research community with the various AR detection catalogues included as part of the ARTMIP project.

Figure 1: Comparison of expert AR identifications from 06 September 2009 of a 25km CAM5 Atmospheric Model Intercomparison Project simulation. The background field shows integrated water vapor, and the green contours show outlines of ARs identified by 15 ARTMIP participants using Lawrence Berkeley National Laboratory’s ClimateNet machine learning software.

O’Brien, T.A., A.E. Payne, C.A. Shields, J. Rutz, S. Brands, C. Castellano, J. Chen, W. Cleveland, M.J. DeFlorio, N. Goldenson, I. Gorodetskaya, H.I. Díaz, K. Kashinath, B. Kawzenuk, S. Kim, M. Krinitskiy, J.M. Lora, B. McClenny, A. Michaelis, J. O’Brien, C.M. Patricola, A.M. Ramos, E.J. Shearer, W. Tung, P. Ullrich, M.F. Wehner, K. Yang, R. Zhang, Z. Zhang, and Y. Zhou, 2020: Detection Uncertainty Matters for Understanding Atmospheric Rivers. Bull. Amer. Meteor. Soc., 0, https://doi.org/10.1175/BAMS-D-19-0348.1

CW3E Publication Notice: Responses and impacts of atmospheric rivers to climate change

CW3E Publication Notice

Responses and impacts of atmospheric rivers to climate change

March 24, 2020

In March 2020 the most substantial review article to date focusing on atmospheric rivers (AR) was published in the first volume of the new journal Nature Reviews: Earth and Environment. The article, led by Ashley Payne (Univ. of Michigan) focuses on climate change dimensions, and was prepared by an international group of scientists, including Scripps/CW3E’s Director, F. Martin Ralph. It provides a useful synopsis of existing literature on ARs, citing over 180 articles. The paper establishes a basic framework for looking at climate change impacts as a combination of sometimes offsetting thermodynamic (moistening of the atmosphere due to warming) and dynamic (e.g., shifting extratropical storms tracks) physical processes. It also addresses impacts of ARs on the hydrological cycle and on hydrologic extremes. The review is summarized schematically in the attached figure that illustrates projected trends in AR counts/locations and their impacts globally, from precipitation and flooding to snow/ice melt.

Finally, the paper identifies key new directions in AR research, ranging from the need for higher resolution modeling, better observations (especially of regions globally where they are lacking), the importance of ARs in polar change, and ultimately of impacts of ARs through their roles in extreme events and in providing beneficial water supply.

Figure 1: Projected changes and impacts in atmospheric rivers. Summary schematic of the main changes to atmospheric-river (AR) characteristics and impacts under warming. Red and blue symbols reveal increases and decreases, respectively; for frequency, red refers to a poleward movement and blue an equatorward movement of landfall. Light red and blue symbols with ‘?’ indicate uncertainty in the projection. Grey symbols indicate unknown changes. Background shading illustrating AR frequency increases is based on Espinoza et al.107. Figure 2 of Payne et al. (2020).

Payne, A.E., M. Demory, L.R. Leung, A.M. Ramos, C.A. Shields, J.J. Rutz., N. Siler, G. Villarini, A. Hall, F.M. Ralph. Responses and impacts of atmospheric rivers to climate change. Nat Rev Earth Environ 1, 143–157 (2020). https://doi.org/10.1038/s43017-020-0030-5