About

David J. Stensrud is a Professor of Meteorology and Atmospheric Science at Penn State University. He earned his Ph.D. in Meteorology from The Pennsylvania State University in 1992 and his M.S. in the same field from the same institution in 1985. His research specializes in mesoscale meteorology and severe weather, with a focus on ensemble forecasting, mesoscale and convective-scale data assimilation, severe weather phenomena, convective-scale predictability, and the North American monsoon. Dr. Stensrud is well known for his studies on ensemble forecasting, which involves groups of numerical weather prediction model forecasts that account for model error and initial condition variability, enabling probabilistic weather predictions. He is currently exploring the use of convective-scale ensembles as a tool for severe weather prediction. His work also emphasizes data assimilation, the process of integrating various observations to form a three-dimensional atmospheric description, particularly for convective-scale models of thunderstorms. Dr. Stensrud has studied physical processes producing damaging windstorms called derechos, developed climatologies of heavy rainfall events over the United States, and evaluated surface data to define cold pool characteristics. His research extends to understanding how urban areas influence severe thunderstorms and how convective systems impact their environment. Additionally, he investigates the predictability of convective phenomena such as thunderstorms and convective lines, and the influence of deep convection on large-scale circulation patterns like the North American monsoon. Dr. Stensrud has authored more than 150 peer-reviewed publications and a textbook titled 'Parameterization Schemes: Keys to Understanding Numerical Weather Models.' He has also served in various professional activities, including chairing workshops, participating in NOAA/NWS teams, and editing special issues on storm-scale data assimilation.

Research topics

• Meteorology
• Environmental science
• Physics
• Atmospheric sciences
• Geology
• Climatology
• Computer Science
• Operations research
• Mechanics
• Geography
• Engineering
• Mathematics
• Remote sensing

Selected publications

• The Multi-University Consortium for Advanced Data Assimilation Research and Education (CADRE)

  Bulletin of the American Meteorological Society · 2026-04-22

  article

  Abstract The Multi-University Consortium for Advanced Data Assimilation Research and Education (CADRE) is a new initiative recently funded by the National Oceanic and Atmospheric Administration (NOAA) to accelerate data assimilation (DA) research, education, and workforce development. Unlike previous initiatives, CADRE fosters end-to-end, direct, and comprehensive collaboration between university faculty and government agencies. It supports innovative DA research, prepares the next-generation DA workforce, and facilitates the transition of DA research to operational applications. CADRE performs a broad scope of cutting-edge research to address multiscale, nonlinear, and coupled Earth system DA challenges to improve short-range (sub-hourly) to seasonal predictions. It achieves this task through innovative data assimilation algorithm development, novel applications of machine learning (ML) in DA, and optimizing the utilization of existing and new in-situ and remotely sensed observations. Beyond research, CADRE establishes a comprehensive education, workforce development, and community building program, which includes a novel graduate student advising model, new university class curriculum development, public training courses, community scientific workshops, an international exchange program, trans-disciplinary partnerships, an outreach program, and promotion of the open sharing of data, code, and educational materials. Through this unique holistic approach, CADRE is set to strengthen both the intellectual and software infrastructure in the broad community for DA research, increase the number of DA scientists with expanded skill sets, and revolutionize forecasting capabilities.

  PublisherDOI

• Synoptic and Mesoscale Variability in Convective Boundary Layer Depth Observations from Dual-Polarization WSR-88D Radars

  Monthly Weather Review · 2025-07-09

  article1st authorCorresponding

  Abstract The convective boundary layer (CBL) is one of the most important and variable layers in the atmosphere, yet observations of the CBL have been limited. Recent results show that the dual-polarization WSR-88D radars can observe CBL depth using differential reflectivity observations on most days with observations available every 10 min or less. We apply a CBL depth tracking method to observations from 48 WSR-88D dual polarization radars spread across CONUS for 2014 and 2022. Results indicate that mean monthly CBL depth over CONUS is smallest in December at 632 m and largest in June at 1606 m. Objective analyses of daily maximum CBL depths show large horizontal variability with synoptic and mesoscale processes contributing to this variability. CBL depths are deeper in the warm sector of midlatitude cyclones compared to surrounding regions, with some days showing anomalies in maximum daily CBL depth of over 1000 m in the warm sector. Mesoscale processes such as lake and sea breezes and precipitation also influence CBL depths. The time of maximum CBL depth shifts throughout the year, being earliest in the winter months and over an hour later in the summer months with regional variations. CBL depth increases most rapidly 1–3 h prior to local noon, with decreases in CBL depth starting 3 h after local noon. Calculating CBL depth routinely from the WSR-88D network will be valuable for air quality and aviation forecasts, model verification, and data assimilation. Significance Statement The boundary layer is the atmospheric layer that is in direct contact with Earth’s surface and is where we spend our lives. The depth of the daytime portion of the boundary layer varies from a few tens of meters early in the morning to over 1000 m by late afternoon before falling back to a shallow depth around sunset. Our ability to observe the depth of this layer has been limited, but upgrades to the U.S. national radar network have opened the door to observing this depth every 10 min on most days. We explore radar estimates of daytime boundary layer depth over 2 years and find that the depth is increased to the south and southwest of low pressure systems. We also find changes in depth from lake and sea breezes and associated with precipitation areas. These new observations allow us for the first time to study simultaneous boundary layer observations from across CONUS and thereby greatly expand our capability to understand the mechanisms that drive changes within this layer.

  PublisherDOI

• Simultaneous Assimilation of Dual-Polarization Radar and All-Sky Satellite Observations to Improve Convection Forecasts

  Monthly Weather Review · 2025-07-29

  article

  Abstract Accurate forecasts of the development and evolution of deep, moist convection in convection-allowing models (CAMs) remain a challenge in part owing to the difficulties inherent in modeling the microphysical and internal structures of convection, which can affect storm mode, intensity, and longevity. We hypothesize that underused Weather Surveillance Radar-1988 Doppler (WSR-88D) and GOES-16 observations can improve forecasts of deep convection in CAM ensembles. Since the upgrade to the national network of WSR-88D radars was completed in 2013, polarimetric radar data offer a wealth of information about the shape, size, and type of hydrometeors present in precipitation. Several distinct polarimetric signatures within deep convection have been identified, such as the differential reflectivity ( Z DR ) column, that can aid significantly in characterizing internal storm structures and improve CAM representation of convection. In addition, GOES-16 infrared all-sky brightness temperatures (BTs) provide complimentary information on cloud structures and cover that radars cannot directly measure. The CAM ensemble in this study uses the Advanced Research version of the Weather Research and Forecasting Model with the High-Resolution Rapid Refresh configuration at 3-km horizontal grid spacing and with 40 ensemble members. Observations are assimilated using an Ensemble Kalman Filter, where the radar and satellite observations are assimilated jointly and separately, and results are compared in four proof-of-concept experiments. Results indicate that the assimilation of BT observations improves forecasts of a severe convective event, which are further improved with the assimilation of Z DR observations. While BT assimilation alone improves the convective forecast, Z DR observations provide additional improvements to updraft helicity tracks, precipitation, and hail forecasts. Significance Statement There are several challenges associated with predictions of severe weather in numerical weather models, including the time and location of thunderstorm initiation as well as the hazards associated with severe thunderstorms, including hail, flash flooding, and tornadoes. This study explores using a combination of underused satellite and radar observations to better define the atmospheric state used to start the weather prediction models, with the hope that this will lead to better forecasts before and during the thunderstorms. Results show that underused observations from routinely available Doppler weather radars and a geostationary satellite, all of which are currently available, can work synergistically to improve forecasts of thunderstorms as well as their hazards.

  PublisherDOI

• Evaluating Planetary Boundary Layer and Land Surface Models via Dual-Polarization WSR-88D and Flux Tower Observations

  Monthly Weather Review · 2025-06-24

  article

  Abstract Recent work has taken advantage of the existing national network of dual-polarization WSR-88D radars to produce accurate daytime planetary boundary layer (PBL) depth estimates for every radar volume scan (roughly every 10 min or less) at WSR-88D sites across the United States. We expand on this work by comparing hourly forecasts of daytime PBL depth from the Mellor–Yamada–Nakanishi–Niino eddy diffusivity–mass flux (MYNN-EDMF) PBL scheme to estimates obtained from WSR-88D observations for calendar year 2022. Forecasts from the operational Rapid Refresh (RAP) model that uses the Advanced Research version of the Weather Research and Forecasting (WRF-ARW) Model are used to generate this large dataset for analysis. We find that MYNN-EDMF forecasts of PBL depth can differ significantly from WSR-88D observations in both timing of growth and maximum depth of the PBL with consistent biases in PBL depth across regions and seasons. These biases are then evaluated through the comparison of parameterized surface sensible and latent heat fluxes to those observed by flux towers located near selected radar sites. We find that errors in predicted PBL depths are strongly correlated with the over-/underestimation of surface sensible heat flux. Further assessment of modeled soil moisture reveals that the land surface model utilized by RAP is likely contributing to PBL depth biases in summer months through erroneous drying of soil, which increases surface sensible heat flux and therefore PBL depth. Significance Statement The boundary layer plays a significant role in many weather phenomena that impact society. Recent work has shown that characteristics of this layer, such as its depth, can be measured by operational weather radars. This study compares these observations to predictions from a weather model to understand that model’s biases. We find that the model consistently overestimates boundary layer depth in summer months and further explore potential causes of this bias. This work will help model developers address model errors and potentially improve weather model performance.

  PublisherDOI

• Estimates of Entrainment Zone Depth across the United States from Dual-Polarization WSR-88D Radar Observations

  Monthly Weather Review · 2025-10-23

  article

  Abstract Recent work has shown that dual-polarization WSR-88D radars can be used to accurately estimate both convective boundary layer depth h and entrainment zone depth Δ h . We build on this work by developing an algorithm to estimate Δ h from WSR-88D data and apply it to a large dataset of WSR-88D observations for the calendar year 2022. Given the existence of a national network of WSR-88Ds, this produces the most spatiotemporally comprehensive dataset of Δ h to date. We find that there are regional and seasonal variations in Δ h with median values near 300 m in January and increasing to 600 m in summer. The values of Δ h are largest in the southeastern CONUS and smallest in the northwestern CONUS. Yearly mean Δ h / h is a maximum at 1000 local time, with values above 0.6, decreasing to ∼0.4 in the afternoon. We also evaluate and extend an existing Δ h parameterization based on lidar data and provide improved fits for days with large Δ h . Significance Statement The boundary layer is the lowest layer of the atmosphere and experiences significant changes in depth throughout the day. The entrainment zone exists at the top of the boundary layer, separating it from the rest of the atmosphere above. Processes within the entrainment zone play a role in boundary layer growth and many weather phenomena. Unfortunately, observations of the depth of the entrainment zone have been very limited. In our study, we apply a new technique of estimating entrainment zone depth from weather radar data across the United States. We also evaluate the performance of a simple model for estimating entrainment zone depth and offer conclusions on how it could be improved.

  PublisherDOI

• Assimilating Novel Boundary Layer Observations from Dual-Polarization Radars to Improve Lower-Tropospheric Moisture and Torrential Rainfall Forecasts

  Monthly Weather Review · 2025-01-31

  article

  Abstract The structure of the planetary boundary layer (PBL) is important for the initiation, development, and organization of convection. High-spatiotemporal-resolution networks that directly observe the PBL structure are currently unavailable. Recent studies discovered that differential reflectivity ( Z DR ) observations from dual-polarization Doppler weather radars in clear-air conditions can be used to characterize the top of the daytime PBL. Compared with other observational platforms that observe the PBL, these Z DR -derived PBL depth observations have high temporal resolution and relatively dense and uniform distributions over the CONUS. Therefore, assimilating these observations could potentially improve the estimation and forecast skill of thermodynamic structures in the lower troposphere. This study examines the impact of assimilating Z DR -derived PBL depth observations on the forecasts of the torrential rainfall and flash flood event in eastern Kentucky on 27–28 July 2022 using a strongly coupled land–atmosphere data assimilation system. The model configuration in the experiments mimics the operational HRRR. Results show that assimilating Z DR -derived PBL depth observations leads to considerable changes in temperature and moisture in the lower troposphere. Soil conditions, including soil moisture and associated surface heat fluxes, are also modified. The assimilation of Z DR -derived PBL depth observations contributes to a better match between model-diagnosed PBL depth with the observations. Subsequently, rainfall forecasts are statistically significantly improved using both gridwise and neighborhood metrics, especially for the most extreme rainfall. Sensitivity experiments also show that the assimilation frequency and the observation errors assigned to Z DR -derived PBL depth observations influence the performance of the rainfall forecasts, which deserve future study. Significance Statement This is the first study that presents a comprehensive investigation on the impact of assimilating planetary boundary layer (PBL) depth observations estimated from dual-polarization NEXRAD radars on the convection-allowing analyses and forecasts of severe weather events. The torrential rainfall and flash flood event that occurred in eastern Kentucky on 27–28 July 2022 is utilized as a case study. Assimilating radar-derived PBL depth observations leads to substantial changes in lower-tropospheric thermodynamic conditions, instability, surface heat fluxes, and soil states. As a result, rainfall forecasts of this event were significantly improved. Since the dual-polarization radar observations used to estimate PBL depth are available over most of the CONUS every 5–10 min, this study demonstrates the great value of these novel observations in data assimilation and numerical weather prediction practices.

  PublisherDOI

• An Investigation of Convective Boundary Layer Depth and Entrainment Zone Properties Using Dual-Polarization Radar and Balloon-Borne Observations

  Journal of Atmospheric and Oceanic Technology · 2024-10-30 · 3 citations

  article

  Abstract Previous work has shown that differential reflectivity Z DR observations from National Weather Service dual-polarization Doppler weather radars (WSR-88Ds) provide accurate estimates of convective boundary layer (CBL) depth when compared with depth estimates from 0000 UTC rawinsonde observations. We extend this work by launching small rawinsondes, called Windsonds, to study Z DR signals throughout the daytime hours. Results show that it can be difficult to identify CBL depth from Z DR alone when biological scatterers are absent. The exploration of other radar variables leads to the use of azimuthal Z DR variance to help in identifying CBL characteristics. A variable that combines both Z DR and azimuthal Z DR variance, called DVar, allows for improved signal identification using the quasi-vertical profile (QVP) method. Furthermore, the QVP channel width is found to be closely tied to the overall entrainment zone (EZ) structure. Results show that the centers and vertical extents of channels of reduced DVar in QVPs correlate well with sounding-observed CBL depth and EZ depth, respectively, across all stages of CBL development and in both clear and cloud-topped CBLs. The QVP approach tends to fail in identifying CBL and EZ depths when the vertical gradient in moisture above the CBL is small. Additionally, we compare the observed EZ depth to various EZ parameterizations and show that the parameterizations generally underestimate EZ depth. We conclude that the ability of WSR-88Ds to sample the CBL should be leveraged to increase our knowledge of CBL properties. Significance Statement The boundary layer is the lowest layer of Earth’s atmosphere and influences many weather-related phenomena. During the day, sunlight warms the surface and the convective boundary layer (CBL) forms. Even though CBL characteristics are important for accurate weather forecasts, current methods of observing the CBL are severely lacking. This study investigates the potential of using dual-polarization weather radars to expand CBL observations. We also evaluate how well simplified CBL models predict certain CBL characteristics and how they could be improved in the future.

  PublisherDOI

• An Automated Approach to Estimating Convective Boundary Layer Depth from Dual-Polarization WSR-88D Radar Observations

  Journal of Atmospheric and Oceanic Technology · 2024-06-18 · 3 citations

  article

  Abstract Convective boundary layer (CBL) depth can be estimated from dual-polarization WSR-88D radars using the product differential reflectivity Z DR because the CBL top is collocated with a local Z DR minimum produced by Bragg scatter at the interface of the CBL and the free troposphere. Quasi-vertical profiles (QVPs) of Z DR are produced for each radar volume scan and profiles from successive times are stitched together to form a time–height plot of Z DR from sunrise to sunset. QVPs of Z DR often show a low- Z DR channel that starts near the ground and rises during the morning and early afternoon, identifying the CBL top. Unfortunately, results show that this channel within the QVP can occasionally be misleading. This motivated creation of a new variable DVar, which combines Z DR with its azimuthal variance and is particularly helpful at identifying the CBL top during the morning hours. Two methods are developed to track the CBL top from QVPs of Z DR and DVar. Although each method has strengths and weaknesses, the best results are found when the two methods are combined using inverse variance weighting. The ability to detect CBL depth from routine WSR-88D radar scans rather than from rawinsondes or lidar instruments would vastly improve our understanding of CBL depth variations in the daytime by increasing the temporal and spatial frequencies of the observations. Significance Statement The daytime convective boundary layer (CBL) can increase in depth from a few hundred to a few thousand meters between sunrise and sunset and is strongly connected to temperature changes at Earth’s surface. Unfortunately, current observations of CBL depth primarily consist of measurements from twice daily rawinsonde launches at 97 locations across the United States. As a result, CBL depth observations lack fine spatial and temporal resolution and miss the daily cycle of CBL growth. This study seeks to fill the gaps in CBL depth observations by developing an automated method to estimate CBL depth from dual-polarization WSR-88D radar observations with a temporal resolution as fine as 5–10 min. These observations will greatly enhance our ability to observe and monitor CBL depth in real time.

  PublisherDOI

• Enhancing Severe Weather Prediction With Microwave All‐Sky Radiance Assimilation: The 10 August 2020 Midwest Derecho

  Geophysical Research Letters · 2024-01-22 · 2 citations

  articleOpen access

  Abstract In this study, we assimilated microwave (MW) all‐sky radiances from low‐Earth‐orbiting satellites and examined their impact on the analyses and forecasts of weather hazards associated with the 10 August 2020 Midwest derecho. Compared with the baseline that assimilated conventional surface and upper‐air observations and infrared (IR) all‐sky radiances from geostationary satellites, the addition of MW all‐sky radiances improved the analyzed and forecasted convection‐stratiform structures of the derecho. Results show that MW all‐sky radiances provided additional information, compared with IR radiances, on hydrometeors within the storm, leading to improved forecasts out to 2 hr with quantitatively more accurate surface gusts. This is the first study to assimilate MW all‐sky radiances for a severe weather event using a convection‐permitting numerical weather prediction model (our model resembles NOAA's High‐Resolution Rapid Refresh), and the results suggest promising avenues for improving severe weather forecasts worldwide in the future.

  PublisherOA PDFDOI

• The Numerical Prediction of Severe Convective Storms: Advances in Research and Applications, Remaining Challenges, and Outlook for the Future

  Elsevier eBooks · 2024-01-01 · 2 citations

  book-chapterCorresponding
  PublisherDOI

Recent grants

• Developing a Climatology of Horizontal Convective Rolls over Oklahoma: Combining Observational Data Sets to Increase Understanding

  NSF · $484k · 2016–2021

• Using Dual-Polarization Radar Observations to Detect Entrainment Zone Depth and Verify Model Forecasts of Convective Boundary Layer Evolution

  NSF · $779k · 2021–2026

Frequent coauthors

• Nusrat Yussouf

  University of Oklahoma

  47 shared

• Kimberly L. Elmore

  37 shared

• Thomas A. Jones

  36 shared

• Jidong Gao

  NOAA National Severe Storms Laboratory

  32 shared

• Xuguang Wang

  University of Oklahoma

  24 shared

• Kenneth Crawford

  24 shared

• Ming Xue

  21 shared

• Michael C. Coniglio

  NOAA National Severe Storms Laboratory

  20 shared

Awards & honors

• Chair, Storm-scale Radar Data Assimilation Workshop, Norman,…
• Member, NOAA/NWS Functional Weather Radar Requirements Integ…
• Guest Editor, Advances in Meteorology, Special Issue on “Sto…
• Commissioner, Scientific and Technological Activities Commis…