Browse

You are looking at 91 - 100 of 9,957 items for :

  • Journal of Applied Meteorology and Climatology x
  • Refine by Access: All Content x
Clear All
René Bodjrenou
,
Jean-Martial Cohard
,
Basile Hector
,
Emmanuel Agnidé Lawin
,
Guillaume Chagnaud
,
Derrick Kwadwo Danso
,
Yekambessoun N’tcha M’po
,
Félicien Badou
, and
Bernard Ahamide

Abstract

In West Africa, climatic data issues, especially availability and quality, remain a significant constraint to the development and application of distributed hydrological modeling. As alternatives to ground-based observations, reanalysis products have received increasing attention in recent years. This study aims to evaluate three reanalysis products, namely, ERA5, Water and Global Change (WATCH) Forcing Data (WFD) ERA5 (WFDE5), and MERRA-2, from 1981 to 2019 to determine their ability to represent four hydrological climates variables over a range of space and time scales in Benin. The variables from the reanalysis products are compared with point station databased metrics Kling–Gupta efficiency (KGE), mean absolute error (MAE), correlation, and relative error in precipitation annual (REPA). The results show that ERA5 presents a better correlation for annual mean temperature (between 0.74 and 0.90) than do WFDE5 (0.63–0.78) and MERRA-2 (0.25–0.65). Both ERA5 and WFDE5 are able to reproduce the observed upward trend of temperature (0.2°C decade−1) in the region. We noted a systematic cold bias of ∼1.3°C in all reanalyses except WFDE5 (∼0.1°C). On the monthly time scale, the temperature of the region is better reproduced by ERA5 and WFDE5 (KGE ≥ 0.80) than by MERRA-2 (KGE < 0.5). At all time scales, WFDE5 produces the best MAE scores for longwave (LW) and shortwave (SW) radiation, followed by ERA5. WFDE5 also provides the best estimates for the annual precipitation (REPA ∈ ]−25, 25[ and KGE ≥ 50% at most stations). ERA5 produces similar results, but MERRA-2 performs poorly in all the metrics. In addition, ERA5 and WFDE5 reproduce the bimodal rainfall regime in southern Benin, unlike MERRA-2, but all products have too many small rainfall events.

Restricted access
Magnus Lindskog
,
Roohollah Azad
,
Siebren de Haan
,
Jesper Blomster
, and
Martin Ridal

Abstract

Meteorological Cooperation on Operational Numeric Weather Prediction (MetCoOp) is a northern European collaboration on operational numerical weather prediction based on a common limited-area, kilometer-scale ensemble system. The initial states of this model are produced using a three-dimensional, variational, data assimilation scheme utilizing a large number of observations from conventional in situ measurements, weather radars, global navigation satellite systems, advanced scatterometer data, and satellite radiances. Since 2019, the MetCoOp system was enhanced by utilization of observations based on selective mode (Mode-S) enhanced surveillance (EHS) reports that are broadcast by aircraft in response to interrogation from air traffic control radar. These observations, obtained from the European Meteorological Aircraft Derived Data Center, are used to derive indirect information of atmospheric wind speed and temperature. The use of these observations compensated for the considerably reduced number of direct aircraft observations encountered as an effect of the COVID-19 pandemic. The MetCoOp observation handling system is described, with emphasis on Mode-S EHS data. The quality of these observations is evaluated, and we show that they are suitable to be used in MetCoOp data assimilation. The impact on average forecast verification scores of the additional Mode-S EHS data is slightly positive. The benefit of using Mode-S EHS was demonstrated for an individual case and also a demonstration of utilizing the Stockholm Arlanda receiver data in assimilation has been performed.

Restricted access
Xinyue Zhan
and
Lei Chen

Abstract

An objective detection and tracking algorithm based on relative vorticity at 850 hPa using National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) Reanalysis-1 data was applied to track cyclones in the Southern Hemisphere during austral winters from 1948 to 2017. The climatological characteristics of extratropical cyclones, including track density, frequency, intensity, lifetime, and their related variabilities, are discussed. The frequency and average lifetime of cyclones have substantially decreased. The average maximum intensity of cyclones has shown an increasing trend over the 70-yr study period. The cyclone track density shows a decreasing trend in lower latitudes, consistent with the region where the upper-troposphere zonal wind weakens. Baroclinicity can explain the increase in cyclone intensity: when a cyclone moves to higher latitudes and enters the region with greater baroclinicity, it strengthens. As there is no discernible increase in cyclogenesis in the medium latitudes (45°–70°S), but significantly less cyclogenesis in lower and higher latitudes, it is hypothesized that there is no clear poleward cyclogenesis shift over the Southern Hemisphere.

Significance Statement

While much is known about the Northern Hemisphere cyclones, few studies have examined how extratropical cyclones have changed in the Southern Hemisphere. We used an automatic tracking algorithm to study the climatological characteristics of extratropical cyclones over the past 70 years and found that the frequency of winter extratropical cyclones has decreased significantly in most parts of the Southern Hemisphere, with the number of intense cyclones increasing. Under global warming conditions, variability in regional low-level baroclinicity and a weakened upper-troposphere jet are likely to be responsible for this change. Future studies may focus on how the increasing autumn sea ice around Antarctica affects polar cyclone activities.

Restricted access
Yelena L. Pichugina
,
Robert M. Banta
,
W. Alan Brewer
,
D. D. Turner
,
V. O. Wulfmeyer
,
E. J. Strobach
,
S. Baidar
, and
B. J. Carroll

Abstract

Low-level jets (LLJs) are an important nocturnal source of wind energy in the U.S. Great Plains. An August 2017 lidar-based field-measurement campaign [the Land–Atmosphere Feedback Experiment (LAFE)] studied LLJs over the central SGP site in Oklahoma and found nearly equal occurrences of the usual southerly jets and postfrontal northeasterly jets—typically rare during this season—for an opportunity to compare the two types of LLJs during this month. Southerly winds were stronger than the northeasterlies by more than 4 m s−1 on average, reflecting a significantly higher frequency of winds stronger than 12 m s−1. The analysis of this dataset has been expanded to other SGP Doppler-lidar sites to quantify the variability of winds and LLJ properties between sites of different land use. Geographic variations of winds over the study area were noted: on southerly wind nights, the winds blew stronger at the highest, westernmost sites by 2 m s−1, whereas on the northeasterly flow nights, the easternmost sites had the strongest wind speeds. Lidar measurements at 5 sites during August 2017, contrasted to the 2016–21 summertime data, revealed unusual wind and LLJ conditions. Temporal hodographs using hourly averaged winds at multiple heights revealed unorganized behavior in the turbulent stable boundary layer (SBL) below the jet nose. Above the nose, some nights showed veering qualitatively similar to inertial oscillation (IO) behavior, but at amplitudes much smaller than expected for an IO, whereas other nights showed little veering. Vertical hodographs had a linear shape in the SBL, indicating little directional shear there, and veering above, resulting in a hook-shaped hodograph with height.

Significance Statement

Doppler-lidar measurements at the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) were used to quantify the variability of winds and low-level jet (LLJ) properties between five sites of different land use and wind regimes across this area. Knowledge of wind and LLJ structure and dynamics is important for many applications, and strong southerly LLJ winds at night are an important resource for wind energy. The analysis of multiyear (2016–21) summertime LLJ parameters provided insight into the LLJ climatology in this part of the Great Plains.

Restricted access
Ying Zhang
,
Xiao Zheng
,
Xiufen Li
,
Jiaxin Lyu
, and
Lanlin Zhao

Abstract

The new-generation multisatellite precipitation algorithm, namely, Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM-IMERG), version 6, provides a high resolution and large spatial extent and can be used to offset the lack of surface observations. This study aimed to evaluate the precipitation detection capability of GPM-IMERG V06 Final Run products in different climatic and topographical regions of China for the 2014–20 period. This study showed that 1) GPM-IMERG could capture the spatial and temporal precipitation distributions in China. At the annual scale, GPM-IMERG performed well, with a correlation coefficient R >0.95 and a relative bias ratio (RBias) between 15.38% and 23.46%. At the seasonal scale, GPM-IMERG performed best in summer. At the monthly scale, GPM-IMERG performed better during the wet season (April–September) (RBias = 7.41%) than during the dry season (RBias = 13.65%). 2) GPM-IMERG performed well in terms of precipitation estimation in Southwest China, Central China, East China, and South China, followed by Northeast China and North China, but it performed poorly in Northwest China and Tibet. 3) The climate zone, followed by elevation, played a leading role in the GPM-IMERG accuracy in China, and the main sources of GPM-IMERG deviation in arid and semiarid regions were missed precipitation and false precipitation. However, the influences of missed precipitation and false precipitation gradually increased with increasing elevation. Despite the obvious differences between the GPM-IMERG and surface precipitation estimates, the study results highlight the potential of GPM-IMERG as a valuable resource for monitoring high-resolution precipitation information that is lacking in many parts of the world.

Significance Statement

The purpose of this study was to better understand the capability of GPM-IMERG for precipitation estimation and the causes of errors. GPM-IMERG performed well when estimating precipitation in Southwest China, Central China, East China, and South China, followed by Northeast China and North China, but it performed poorly in Northwest China and Tibet. Climate, followed by elevation, played a leading role in GPM-IMERG accuracy in China. Our results could provide a greater understanding of the accuracy of GPM-IMERG precipitation estimation in the different regions of China and can be applied to water resource management, afforestation (or reforestation) projects, and so on, in areas worldwide where meteorological stations are scarce.

Restricted access
Marc Mandement
,
Pierre Kirstetter
, and
Heather Reeves

Abstract

The accuracy and uncertainty of radar echo-top heights estimated by ground-based radars remain largely unknown despite their critical importance for applications ranging from aviation weather forecasting to severe weather diagnosis. Because the vantage point of space is more suited than that of ground-based radars for the estimation of echo-top heights, the use of spaceborne radar observations is explored as an external reference for cross comparison. An investigation has been carried out across the conterminous United States by comparing the NOAA/National Severe Storms Laboratory Multi-Radar Multi-Sensor (MRMS) system with the space-based radar on board the NASA–JAXA Global Precipitation Measurement satellite platform. No major bias was assessed between the two products. An annual cycle of differences is found, driven by an underestimation of the stratiform cloud echo-top heights and an overestimation of the convective ones. The investigation of the systematic biases for different radar volume coverage patterns (VCP) shows that scanning strategies with fewer tilts and greater voids as VCP 21/121/221 contribute to overestimations observed for high MRMS tops. For VCP 12/212, the automated volume scan evaluation and termination (AVSET) function increases the radar cone of silence, causing overestimations when the echo top lies above the highest elevation scan. However, it seems that for low echo tops the shorter refresh rates contribute to mitigate underestimations, especially in stratiform cases.

Restricted access
Free access
Brian N. Belcher
,
Arthur T. DeGaetano
,
Forrest J. Masters
,
Jay Crandell
, and
Murray J. Morrison

Abstract

A method is presented to obtain the climatology of extreme wind speeds coincident with the occurrence of rain. The simultaneous occurrence of wind and rain can force water through building wall components such as windows, resulting in building damage and insured loss. To quantify this hazard, extreme value distributions are fit to peak 3-s wind speed data recorded during 1-min intervals with specific reported rain intensities. This improves upon previous attempts to quantify the wind-driven rain hazard that computed wind speed and rainfall-intensity probabilities independently and used hourly data that cannot assure the simultaneous occurrence of peak wind that represents only a several-second interval within the hour and rain that is accumulated over the entire hour. The method is applied across the southeastern United States, where the wind-driven rain hazard is most pronounced. For the lowest rainfall intensities, the computed wind speed extremes agree with published values that ignore rainfall occurrence. Such correspondence is desirable for aligning the rain-intensity-dependent wind speed return periods with established extreme wind statistics. Maximum 50-yr return-period wind speeds in conjunction with rainfall intensities ≥0.254 mm min−1 exceed 45 m s−1 in a swath from Oklahoma to the Gulf Coast and at stations along the immediate Atlantic coast. For rainfall intensities >2.54 mm min−1 maximum, 50-yr return-period wind speeds decrease to 35 m s−1 but occur over a similar area. The methodology is also applied to stations outside the Southeast to demonstrate its applicability for incorporating the wind-driven rain hazard in U.S. building standards.

Significance Statement

Rainfall driven horizontally by strong winds can penetrate building components and cladding. If unmanaged, this can directly damage the building and its contents and become a substantial component of insured losses to buildings. A climatology of wind-driven rain is developed from recently available 1-min weather observations that better represent the joint occurrence of the extremes that define wind-driven rain occurrence than hourly data. This work is a first implementation of 1-min data into extreme-value statistical models, providing a basis for including wind-driven rain in United States building codes. This inclusion would be most significant in the hurricane-prone regions of the southeastern United States. The omission of wind-driven rain in U.S. building codes contrasts to its inclusion in Europe and Canada.

Restricted access
R. A. Wakefield
,
D. D. Turner
,
T. Rosenberger
,
T. Heus
,
T. J. Wagner
,
J. Santanello
, and
J. Basara

Abstract

Land–atmosphere interactions play a critical role in both the atmospheric water and energy cycles. Changes in soil moisture and vegetation alter the partitioning of surface water and energy fluxes, influencing diurnal evolution of the planetary boundary layer (PBL). The mixing-diagram framework has proven useful in understanding the evolution of the heat and moisture budget within the convective boundary layer (CBL). We demonstrate that observations from the Department of Energy Atmospheric Radiation Measurement (ARM) Southern Great Plains site provide all of the needed inputs needed for the mixing-diagram framework, allowing us to quantify the impact from the surface fluxes, advection, radiative heating, encroachment, and entrainment on the evolution of the CBL. Profiles of temperature and humidity retrieved from the ground-based infrared spectrometer [Atmospheric Emitted Radiance Interferometer (AERI)] are a critical component in this analysis. Large-eddy simulation results demonstrate that mean mixed-layer values derived are shown to be critical to close the energy and moisture budgets. A novel approach demonstrated here is the use of network of AERIs and Doppler lidars to quantify the advective fluxes of heat and moisture. The framework enables the estimation of the entrainment fluxes as a residual, providing a way to observe the entrainment fluxes without using multiple lidar systems. The high temporal resolution of the AERI observations enables the morning, midday, and afternoon evolution of the CBL to be quantified. This work provides a new way to use observations in this framework to evaluate weather and climate models.

Significance Statement

The energy and moisture budget of the planetary boundary layer (PBL) is influenced by multiple sources, and accurately representing this evolution in numerical models is critical for weather forecasts and climate predictions. The mixing-diagram approach, driven by profiling observations as illustrated here, provides a powerful way to quantify the contributions from each of these sources. In particular, the energy and moisture mixed into the PBL from above the PBL can be determined accurately from ground-based remote sensors using this approach.

Open access
Eric Goldenstern
and
Christian Kummerow

Abstract

Despite its long history, improving upon current precipitation estimation techniques remains an active area of research. While many methods exist to assess precipitation, the use of satellites has allowed for near-global observation. However, satellites do not directly sense precipitation, resulting in retrieval uncertainties. Analysis of these uncertainties is typically conducted through validation studies, which, while necessary, are sensitive to local conditions. As such, predicting retrieval uncertainties where there is no validation data remains a challenge. In this study, we propose a method by which validation statistics can be extended to other regions. Using a neural network–style retrieval, the Geostationary Operational Environmental Satellite–16 (GOES-16) Precipitation Estimator using Convolutional Neural Networks (GPE-CNN), we show that, by exploiting the information content of both the satellite and ancillary meteorological data, one can predict large-scale retrieval behaviors over other regions without the need for that region’s validation data. By developing classes using satellite information content, we demonstrate bias prediction improvement of up to 83% relative to a simple extension of mean bias. Including relative humidity information improves the overall prediction by up to 98% relative to the original mean bias. Although limited in scope, this method presents a pathway toward characterizing uncertainties on a broader scale.

Restricted access