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Free access
Junjun Cao
,
Fu Guan
,
Xiang Zhang
,
Won-Ho Nam
,
Guoyong Leng
,
Haoran Gao
,
Qingqing Ye
,
Xihui Gu
,
Jiangyuan Zeng
,
Xu Zhang
,
Tailai Huang
, and
Dev Niyogi

Abstract

Predicting drought severity is essential for drought management and early warning systems. Although numerous physical model-based and data-driven methods have been put forward for drought prediction, their abilities are largely constrained by data requirements and modeling complexity. There remains a challenging task to efficiently predict categorial drought, especially for the U.S. Drought Monitor (USDM). Aiming at this issue, multiple Markov chains for USDM-based categorial drought prediction are successfully proposed and evaluated in this paper. In particular, this study concentrated on how the Markov order, step size, and training set length affected prediction accuracy (PA). According to experiments from 2000 to 2021, it was found that the 1-step and first-order Markov models had the best accuracy in predicting droughts up to 4 weeks ahead. The PA steadily dropped with increasing step size, and the average accuracy at monthly scale was 88%. In terms of seasonal variability, summer (July–August) had the lowest PA while winter had the highest (January–February). In comparison with the western region, the PA in the eastern United States is 25% higher. Moreover, the length of the training set had an obvious impact on the PA of the model. The PA in 1-step prediction was 87% and 78% under 20- and 5-yr training sets, respectively. The results of the study showed that Markov models predicted categorical drought with high accuracy in the short term and showed different performances on time and space scales. Proper use of Markov models would help disaster managers and policy makers to put mitigation policies and measures into practice.

Restricted access
Riku Shimizu
,
Shoichi Shige
,
Toshio Iguchi
,
Cheng-Ku Yu
, and
Lin-Wen Cheng

Abstract

The Dual-Frequency Precipitation Radar (DPR), which consists of a Ku-band precipitation radar (KuPR) and a Ka-band precipitation radar (KaPR) on board the GPM Core Observatory, cannot observe precipitation at low altitudes near the ground contaminated by surface clutter. This near-surface region is called the blind zone. DPR estimates the clutter-free bottom (CFB), which is the lowest altitude not included in the blind zone, and estimates precipitation at altitudes higher than the CFB. High CFBs, which are common over mountainous areas, represent obstacles to detection of shallow precipitation and estimation of low-level enhanced precipitation. We compared KuPR data with rain gauge data from Da-Tun Mountain of northern Taiwan acquired from March 2014 to February 2020. A total of 12 cases were identified in which the KuPR missed some rainfall with intensity of >10 mm h−1 that was observed by rain gauges. Comparison of KuPR profile and ground-based radar profile revealed that shallow precipitation in the KuPR blind zone was missed because the CFB was estimated to be higher than the lower bound of the range free from surface echoes. In the original operational algorithm, CFB was estimated using only the received power data of the KuPR. In this study, the CFB was identified by the sharp increase in the difference between the received powers of the KuPR and the KaPR at altitude affected by surface clutter. By lowering the CFB, the KuPR succeeded in detection and estimation of shallow precipitation.

Significance Statement

The Dual-Frequency Precipitation Radar (DPR) on board the GPM Core Observatory cannot capture precipitation in the low-altitude region near the ground contaminated by surface clutter. This region is called the blind zone. The DPR estimates the clutter-free bottom (CFB), which is the lower bound of the range free from surface echoes, and uses data higher than CFB. DPR consists of a Ku-band precipitation radar (KuPR) and a Ka-band precipitation radar (KaPR). KuPR missed some shallow precipitation more than 10 mm h−1 in the blind zone over Da-Tun Mountain of northern Taiwan because of misjudged CFB estimation. Using both the KuPR and the KaPR, we improved the CFB estimation algorithm, which lowered the CFB, narrowed the blind zone, and improved the capability to detect shallow precipitation.

Open access
Troy J. Zaremba
,
Robert M. Rauber
,
Bart Geerts
,
Jeffrey R. French
,
Sarah A. Tessendorf
,
Lulin Xue
,
Katja Friedrich
,
Courtney Weeks
,
Roy M. Rasmussen
,
Melvin L. Kunkel
, and
Derek R. Blestrud

Abstract

This paper examines the controls on supercooled liquid water content (SLWC) and drop number concentrations (Nt ,CDP) over the Payette River basin during the Seeded and Natural Orographic Wintertime Clouds: The Idaho Experiment (SNOWIE) campaign. During SNOWIE, 27.4% of 1-Hz in situ cloud droplet probe samples were in an environment containing supercooled liquid water (SLW). The interquartile range of SLWC, when present, was found to be 0.02–0.18 g m−3 and 13.3–37.2 cm−3 for Nt ,CDP, with the most extreme values reaching 0.40–1.75 g m−3 and 150–320 cm−3 in isolated regions of convection and strong shear-induced turbulence. SLWC and Nt ,CDP distributions are shown to be directly related to cloud-top temperature and ice particle concentrations, consistent with past research over other mountain ranges. Two classes of vertical motions were analyzed as potential controls on SLWC and Nt ,CDP, the first forced by the orography and fixed in space relative to the topography (stationary waves) and the second transient, triggered by vertical shear and instability within passing synoptic-scale cyclones. SLWC occurrence and magnitudes, and Nt ,CDP associated with fixed updrafts were found to be normally distributed about ridgelines when SLW was present. SLW was more likely to form at low altitudes near the terrain slope associated with fixed waves due to higher mixing ratios and larger vertical air parcel displacements at low altitudes. When considering transient updrafts, SLWC and Nt ,CDP appear more uniformly distributed over the flight track with little discernable terrain dependence as a result of time and spatially varying updrafts associated with passing weather systems. The implications for cloud seeding over the basin are discussed.

Restricted access
Douglas Schuster
and
Michael Friedman
Open access
Amanda M. Murphy
and
Cameron R. Homeyer

Abstract

Forecasting tornadogenesis remains a difficult problem in meteorology, especially for short-lived, predominantly nonsupercellular tornadic storms embedded within mesoscale convective systems (MCSs). This study compares populations of tornadic nonsupercellular MCS storm cells with their nontornadic counterparts, focusing on nontornadic storms that have similar radar characteristics to tornadic storms. Comparisons of single-polarization radar variables during storm lifetimes show that median values of low-level, midlevel, and column-maximum azimuthal shear, as well as low-level radial divergence, enable the highest degree of separation between tornadic and nontornadic storms. Focusing on low-level azimuthal shear values, null storms were randomly selected such that the distribution of null low-level azimuthal shear values matched the distribution of tornadic values. After isolating the null cases from the nontornadic population, signatures emerge in single-polarization data that enable discrimination between nontornadic and tornadic storms. In comparison, dual-polarization variables show little deviation between storm types. Tornadic storms both at tornadogenesis and at a 20-min lead time show collocation of the primary storm updraft with enhanced near-surface rotation and convergence, facilitating the nonmesocyclonic tornadogenesis processes.

Restricted access
Free access
W. James Steenburgh
,
Julie A. Cunningham
,
Philip T. Bergmaier
,
Bart Geerts
, and
Peter Veals

Abstract

Potential factors affecting the inland penetration and orographic modulation of lake-effect precipitation east of Lake Ontario include the environmental (lake, land, and atmospheric) conditions, mode of the lake-effect system, and orographic processes associated with flow across the downstream Tug Hill Plateau (herein Tug Hill), Black River valley, and Adirondack Mountains (herein Adirondacks). In this study we use data from the KTYX WSR-88D, ERA5 reanalysis, New York State Mesonet, and Ontario Winter Lake-effect Systems (OWLeS) field campaign to examine how these factors influence lake-effect characteristics with emphasis on the region downstream of Tug Hill. During an eight-cool-season (16 November–15 April) study period (2012/13–2019/20), total radar-estimated precipitation during lake-effect periods increased gradually from Lake Ontario to upper Tug Hill and decreased abruptly where the Tug Hill escarpment drops into the Black River valley. The axis of maximum precipitation shifted poleward across the northern Black River valley and into the northwestern Adirondacks. In the western Adirondacks, the heaviest lake-effect snowfall periods featured strong, near-zonal boundary layer flow, a deep boundary layer, and a single precipitation band aligned along the long-lake axis. Airborne profiling radar observations collected during OWLeS IOP10 revealed precipitation enhancement over Tug Hill, spillover and shadowing in the Black River valley where a resonant lee wave was present, and precipitation invigoration over the western Adirondacks. These results illustrate the orographic modulation of inland-penetrating lake-effect systems downstream of Lake Ontario and the factors favoring heavy snowfall over the western Adirondacks.

Significance Statement

Inland penetrating lake-effect storms east of Lake Ontario affect remote rural communities, enable a regional winter-sports economy, and contribute to a snowpack that contributes to runoff and flooding during thaws and rain-on-snow events. In this study we illustrate how the region’s three major geographic features—Tug Hill, the Black River valley, and the western Adirondacks—affect the characteristics of lake-effect precipitation, describe the factors contributing to heavy snowfall over the western Adirondacks, and provide an examples of terrain effects in a lake-effect storm observed with a specially instrumented research aircraft.

Restricted access
Daniel P. Greenway
,
Tracy Haack
, and
Erin E. Hackett

Abstract

This study investigates the use of numerical weather prediction (NWP) ensembles to aid refractivity inversion problems during surface ducting conditions. Thirteen sets of measured thermodynamic atmospheric data from an instrumented helicopter during the Wallops Island field experiment are fit to a two-layer parametric surface duct model to characterize the duct. This modeled refractivity is considered “ground truth” for the environment and is used to generate the synthetic radar propagation loss field that then drives the inversion process. The inverse solution (refractivity derived from the synthetic radar data) is compared with this ground truth refractivity. For the inversion process, parameters of the two-layer model are iteratively estimated using genetic algorithms to determine which parameters likely produced the synthetic radar propagation field. Three numerical inversion experiments are conducted. The first experiment utilizes a randomized set of two-layer model parameters to initialize the inversion process, while the second experiment initializes the inversion using NWP ensembles, and the third experiment uses NWP ensembles to both initialize and restrict the parameter search intervals used in the inversion process. The results show that incorporation of NWP data benefits the accuracy and speed of the inversion result. However, in a few cases, an extended NWP ensemble forecast period was needed to encompass the ground truth parameters in the restricted search space. Furthermore, it is found that NWP ensemble populations with smaller spreads are more likely to hinder the inverse process than to aid it.

Restricted access
Athanasios Ntoumos
,
Panos Hadjinicolaou
,
George Zittis
,
Katiana Constantinidou
,
Anna Tzyrkalli
, and
Jos Lelieveld

Abstract

We assess the sensitivity of the Weather Research and Forecasting (WRF) Model to the use of different planetary boundary layer (PBL) parameterizations focusing on air temperature and extreme heat conditions. This work aims to evaluate the performance of the WRF Model in simulating temperatures across the Middle East–North Africa (MENA) domain, explain the model biases resulting from the choice of different PBL schemes, and identify the best-performing configuration for the MENA region. Three different PBL schemes are used to downscale the ECMWF ERA-Interim climate over the MENA region at a horizontal resolution of 24 km, for the period 2000–10. These are the Mellor–Yamada–Janjić (MYJ), Yonsei University (YSU), and the asymmetric convective model, version 2 (ACM2). For the evaluation of the WRF runs, we used related meteorological variables from the ERA5 reanalysis, including summer maximum and minimum 2-m air temperature and heat extreme indices. Our results indicate that simulations tend to overestimate maximum temperatures and underestimate minimum temperatures, and we find that model errors are very dependent on the geographic location. The possible physical causes of model biases are investigated through the analysis of additional variables (such as boundary layer height, moisture, and heat fluxes). It is shown that differences among the PBL schemes are associated with differences in vertical mixing strength, which alters the magnitude of the entrainment of free-tropospheric air into the PBL. The YSU is found to be the best-performing scheme, and it is recommended in WRF climate simulations for the MENA region.

Open access