Abstract

Because weather radar data are commonly employed in automated weather applications, it is necessary to censor nonmeteorological contaminants, such as bioscatter, instrument artifacts, and ground clutter, from the data. With the operational deployment of a widespread polarimetric S-band radar network in the United States, it has become possible to fully utilize polarimetric data in the quality control (QC) process. At each range gate, a pattern vector consisting of the values of the polarimetric and Doppler moments, and local variance of some of these features, as well as 3D virtual volume features, is computed. Patterns that cannot be preclassified based on correlation coefficient ρ _HV, differential reflectivity Z _dr, and reflectivity are presented to a neural network that was trained on historical data. The neural network and preclassifier produce a pixelwise probability of precipitation at that range gate. The range gates are then clustered into contiguous regions of reflectivity, with bimodal clustering carried out close to the radar and clustering based purely on spatial connectivity farther away from the radar. The pixelwise probabilities are averaged within each cluster, and the cluster is either retained or censored depending on whether this average probability is greater than or less than 0.5. The QC algorithm was evaluated on a set of independent cases and found to perform well, with a Heidke skill score (HSS) of about 0.8. A simple gate-by-gate classifier, consisting of three simple rules, is also introduced in this paper and can be used if the full QC method is not able to be applied. The simple classifier has an HSS of about 0.6 on the independent dataset.

Abstract

The Mariana Ridge is one of the prominent mixing hotspots of the open ocean. The high-resolution underway marine seismic reflection technique provides an improved understanding of the spatiotemporal continuous map of ocean turbulent mixing. Using this novel technique, this study quantifies the diapycnal diffusivity of the subthermocline (300–1200-m depth) turbulence around the Mariana Ridge. The autotracked wave fields on seismic images allow us to derive the dissipation rate ε and diapycnal diffusivity K _ρ based on the Batchelor model, which relates the horizontal slope spectra with +1/3 slope to the inertial convective turbulence regime. Diffusivity is locally intensified around the seamounts exceeding 10⁻³ m² s⁻¹ and gradually decreases to 10⁻⁵–10⁻⁴ m² s⁻¹ in ~60-km range, a distance that may be associated with the internal tide beam emanating paths. The overall pattern suggests a large portion of the energy dissipates locally and a significant portion dissipates in the far field. Empirical diffusivity models K _ρ (x) and K _ρ (z), varying with the distance from seamounts and the height above seafloor, respectively, are constructed for potential use in ocean model parameterization. Geographic distributions of both the vertically averaged dissipation rate and diffusivity show tight relationships with the topography. Additionally, a strong agreement of the dissipation results between seismic observation and numerical simulation is found for the first time. Such an agreement confirms the suitability of the seismic method in turbulence quantification and suggests the energy cascade from large-scale tides to small-scale turbulence via possible mechanisms of local direct tidal dissipation, near-local wave–wave interactions, and far-field radiating and breaking.

Abstract

Seasonal killing-frost frequency (KFF) during the cool/overwintering-crop growing season is important for the Canadian agricultural sector to prepare and respond to such extreme agrometeorological events. On the basis of observed daily surface air temperature across Canada for 1957–2007, this study found that more than 86% of the total killing-frost events occur in April–May and exhibit consistent variability over south-central Canada, the country’s major agricultural region. To quantify the KFF year-to-year variations, a simple index is defined as the mean KFF of the 187 temperature stations in south-central Canada. The KFF variability is basically dominated by two components: the decadal component with a peak periodicity around 11 yr and the interannual component of 2.5–3.8 yr. A statistical method called partial least squares (PLS) regression is utilized to uncover principal sea surface temperature (SST) modes in the winter preceding the KFF anomalies. It is found that most of the leading SST modes resemble patterns of El Niño–Southern Oscillation (ENSO) and/or the Pacific decadal oscillation (PDO). This indicates that ENSO and the PDO might be two dominant factors for the KFF variability. From a 41-yr training period (1957–97), a PLS seasonal prediction model is established, and 1-month-lead real-time forecasts are performed for the validation period of 1998–2007. A promising skill level is obtained. For the KFF variability, the prediction skill of the PLS model is comparable to or even better than the newly developed Canadian Seasonal to Interannual Prediction System (CanSIPS), which is a state-of-the-art global coupled dynamical system.

Abstract

Mesoscale eddies are a mechanism for ocean energy transfer, and identifying them on a global scale provides a means of exploring ocean mass and energy exchange between ocean basins. There are many widely used model-driven methods for detecting mesoscale eddies; however, these methods are not fully robust or generalizable. This study applies a data-driven method and proposes a mesoscale detection network based on the extraction of eddy-related spatiotemporal information from multisource remote sensing data. Focusing on the northwest Pacific, the study first analyzes mesoscale eddy characteristics using a combination of gridded data for the absolute dynamic topography (ADT), sea surface temperature (SST), and absolute geostrophic velocity (UVG). Then, a deep learning network with a dual-attention mechanism and a convolutional long short-term memory module is proposed, which can deeply exploit spatiotemporal feature relevance while encoding and decoding information in the gridded data. Based on the analysis of mesoscale eddy characteristics, ADT and UVG gridded data are selected to be the inputs for the detection network. The experiments show that the accuracy of the proposed network reaches 93.38%, and the weighted mean dice coefficient reaches 0.8918, which is a better score than those achieved by some of the detection networks proposed in previous studies, including U-Net, SymmetricNet, and ResU-Net. Moreover, compared with the model-driven approach used to generate the ground-truth dataset, the network method proposed here demonstrates better performance in detecting mesoscale eddies at smaller scales, partially addressing the problem of ghost eddies.

Abstract

A method of detecting atmospheric ducts using a wind profiler radar (WPR) and a radio acoustic sounding system (RASS) is proposed. The method uses the RASS to measure the virtual temperature profile and calculate the Brunt–Väisälä frequency; it also uses the WPR to measure the spectral width of the atmosphere and the atmospheric refractive index structure constant. Then the profile of the atmospheric refractive index gradient and modified refractivity are calculated using virtual temperature, spectral width, and the atmospheric refractive index structure constant. Finally, the height and intensity of the atmospheric duct are calculated to achieve continuous monitoring of the atmospheric duct. To verify the height and intensity of the atmospheric duct, comparison experiments between WPR-RASS and radiosondes were carried out from June 2014 to June 2015 in Dalian, Liaoning Province, China. The results show that the profile of modified refractivity by WPR-RASS has exactly the same trend as the radiosondes, the two methods have a good consistency, and the atmospheric duct value from WPR-RASS is in good agreement with that from radiosondes.

Abstract

To obtain accurate radar quantitative precipitation estimation (QPE) for extreme rainfall events such as land-falling typhoon systems in complex terrain, a new method was developed for C-band polarimetric radars. The new methodology includes a correction method based on vertical profiles of the specific differential propagation phase (VPSDP) for low-level blockage and an optimal relation between rainfall rate ( ) and the specific differential phase ( ). In the VPSDP-based correction approach, a screening process is applied to fields, where missing or unreliable data from lower tilts caused by severe beam blockage are replaced with data from upper and unblocked tilts. The data from upper tilts are adjusted to account for variations in the vertical profile of . The corrected field is then used for rain-rate estimations. To acquire an accurate QPE result, a new relation for C-band polarimetric radars was derived through simulations using drop size distribution (DSD) and drop shape relation (DSR) observations from typhoon systems in Taiwan. The VPSDP-based correction method with the new relation was evaluated using the typhoon cases of Morakot (2009) and Fanapi (2010).

Abstract

The U.S. Geological Survey (USGS) land use (LU) data employed in the Weather Research and Forecasting (WRF) model classify most LU types in Taiwan as mixtures of irrigated cropland and forest, which is not an accurate representation of current conditions. The WRF model released after version 3.1 provides an alternative LU dataset retrieved from 2001 Moderate Resolution Imaging Spectroradiometer (MODIS) satellite products. The MODIS data correctly identify most LU-type distributions, except that they represent western Taiwan as being extremely urbanized. A new LU dataset, obtained using 2007 Système Probatoire d’Observation de la Terre (SPOT) satellite imagery [from the National Central University of Taiwan (NCU)], accurately shows the major metropolitan cities as well as other land types. Three WRF simulations were performed, each with a different LU dataset. Owing to the overestimation of urban area in the MODIS data, WRF-MODIS overpredicts daytime temperatures in western Taiwan. Conversely, WRF-USGS underpredicts daytime temperatures. The temperature variation estimated by WRF-NCU falls between those estimated by the other two simulations. Over the ocean, WRF-MODIS predicts the strongest onshore sea breezes, owing to the enhanced temperature gradient between land and sea, while WRF-USGS predicts the weakest onshore flow. The intensity of the onshore breeze predicted by WRF-NCU is between those predicted by WRF-MODIS and WRF-USGS. Over Taiwan, roughness length is the key parameter influencing wind speed. WRF-USGS significantly overpredicts the surface wind speed owing to the shorter roughness length of its elements, while the surface wind speeds estimated by WRF-NCU and WRF-MODIS are in better agreement with the observed data.

Abstract

In this study, ensemble seasonal predictions of the Arctic Oscillation (AO) were conducted for 51 winters (1948–98) using a simple global atmospheric general circulation model. A means of estimating a priori the predictive skill of the AO ensemble predictions was developed based on the relative entropy (R) of information theory, which is a measure of the difference between the forecast and climatology probability density functions (PDFs). Several important issues related to the AO predictability, such as the dominant precursors of forecast skill and the degree of confidence that can be placed in an individual forecast, were addressed. It was found that R is a useful measure of the confidence that can be placed on dynamical predictions of the AO. When R is large, the prediction is likely to have a high confidence level whereas when R is small, the prediction skill is more variable. A small R is often accompanied by a relatively weak AO index. The value of R is dominated by the predicted ensemble mean. The relationship identified here, between model skills and the R of an ensemble prediction, offers a practical means of estimating the confidence level of a seasonal forecast of the AO using the dynamical model.

Through an analysis of the global sea surface temperature (SST) forcing, it was found that the winter AO-related R is correlated significantly with the amplitude of the SST anomalies over the tropical central Pacific and the North Pacific during the previous October. A large value of R is usually associated with strong SST anomalies in the two regions, whereas a poor prediction with a small R indicates that SST anomalies are likely weak in these two regions and the observed AO anomaly in the specific winter is likely caused by atmospheric internal dynamics.

Abstract

Over the last two decades, the Central Weather Bureau of Taiwan and the U.S. National Severe Storms Laboratory have been involved in a research and development collaboration to improve the monitoring and prediction of river flooding, flash floods, debris flows, and severe storms for Taiwan. The collaboration resulted in the Quantitative Precipitation Estimation and Segregation Using Multiple Sensors (QPESUMS) system. The QPESUMS system integrates observations from multiple mixed-band weather radars, rain gauges, and numerical weather prediction model fields to produce high-resolution (1 km) and rapid-update (10 min) rainfall and severe storm monitoring and prediction products. The rainfall products are widely used by government agencies and emergency managers in Taiwan for flood and mudslide warnings as well as for water resource management. The 3D reflectivity mosaic and QPE products are also used in high-resolution radar data assimilation and for the verification of numerical weather prediction model forecasts. The system facilitated collaborations with academic communities for research and development of radar applications, including quantitative precipitation estimation and nowcasting. This paper provides an overview of the operational QPE capabilities in the Taiwan QPESUMS system.

Abstract

A prototype quantitative precipitation estimate (QPE) algorithm that utilizes specific attenuation A and specific differential phase K _DP was developed for inclusion into the Multi-Radar Multi-Sensor (MRMS) system and the Weather Surveillance Radar-1988 Doppler (WSR-88D) network. Special attention is given to the optimization of the factor α used for computation of a path-integrated attenuation from a total span of differential phase along the propagation path in rain. It is suggested to estimate α from a slope of the Z _DR dependence on Z in rain. The use of real-time adjusted α allows us to capture the variations of the drop size distributions, and therefore improve the QPE accuracy. It is demonstrated that the factor α is generally higher for tropical rain type compared to continental rain. Since the R(A) approach is only valid for pure rainfall, the R(K _DP) relation is suggested as a complement in areas of hail contamination. The paper contains a description of the basic version of the R(A) and R(K _DP) algorithm and recommendations for its further optimization.