In this study, we present RainNet, a deep convolutional neural network
for radar-based precipitation nowcasting. Its design was inspired by
the U-Net and SegNet families of deep learning models, ...which were
originally designed for binary segmentation tasks. RainNet was trained
to predict continuous precipitation intensities at a lead time of 5 min, using several years of quality-controlled weather radar
composites provided by the German Weather Service (DWD). That data set
covers Germany with a spatial domain of 900 km×900 km
and has a resolution of 1 km in space and 5 min in
time. Independent verification experiments were carried out on 11
summer precipitation events from 2016 to 2017. In order to achieve
a lead time of 1 h, a recursive approach was implemented by using
RainNet predictions at 5 min lead times as model inputs for
longer lead times. In the verification experiments, trivial Eulerian
persistence and a conventional model based on optical flow served as
benchmarks. The latter is available in the rainymotion
library and had previously been shown to outperform DWD's operational
nowcasting model for the same set of verification events. RainNet significantly outperforms the benchmark models at all lead
times up to 60 min for the routine verification metrics mean
absolute error (MAE) and the critical success index (CSI) at intensity
thresholds of 0.125, 1, and 5 mm h−1. However, rainymotion turned out to be superior in predicting the exceedance of higher
intensity thresholds (here 10 and 15 mm h−1). The limited
ability of RainNet to predict heavy rainfall intensities is an
undesirable property which we attribute to a high level of spatial
smoothing introduced by the model. At a lead time of 5 min, an
analysis of power spectral density confirmed a significant loss of
spectral power at length scales of 16 km and below. Obviously,
RainNet had learned an optimal level of smoothing to produce a nowcast
at 5 min lead time. In that sense, the loss of spectral power
at small scales is informative, too, as it reflects the limits of
predictability as a function of spatial scale. Beyond the lead time of
5 min, however, the increasing level of smoothing is a mere
artifact – an analogue to numerical diffusion – that is not
a property of RainNet itself but of its recursive application. In the
context of early warning, the smoothing is particularly unfavorable
since pronounced features of intense precipitation tend to get lost
over longer lead times. Hence, we propose several options to address
this issue in prospective research, including an adjustment of the
loss function for model training, model training for longer lead
times, and the prediction of threshold exceedance in terms of a binary
segmentation task. Furthermore, we suggest additional input data that
could help to better identify situations with imminent precipitation
dynamics. The model code, pretrained weights, and training data are
provided in open repositories as an input for such future studies.
Decline of the North American avifauna Rosenberg, Kenneth V; Dokter, Adriaan M; Blancher, Peter J ...
Science (American Association for the Advancement of Science),
10/2019, Letnik:
366, Številka:
6461
Journal Article
Recenzirano
Odprti dostop
Species extinctions have defined the global biodiversity crisis, but extinction begins with loss in abundance of individuals that can result in compositional and functional changes of ecosystems. ...Using multiple and independent monitoring networks, we report population losses across much of the North American avifauna over 48 years, including once-common species and from most biomes. Integration of range-wide population trajectories and size estimates indicates a net loss approaching 3 billion birds, or 29% of 1970 abundance. A continent-wide weather radar network also reveals a similarly steep decline in biomass passage of migrating birds over a recent 10-year period. This loss of bird abundance signals an urgent need to address threats to avert future avifaunal collapse and associated loss of ecosystem integrity, function, and services.
Weather radars have been widely used to detect and quantify precipitation and nowcast severe weather for more than 50 years. Operational weather radars generate huge three-dimensional datasets that ...can accumulate to terabytes per day. So it is essential to review what can be done with existing vast amounts of data, and how we should manage the present datasets for the future climatologists. All weather radars provide the ref lectivity factor, and this is the main parameter to be archived. Saving reflectivity as volumetric data in the original spherical coordinates allows for studies of the three-dimensional structure of precipitation, which can be applied to understand a number of processes, for example, analyzing hail or thunderstorm modes. Doppler velocity and polarimetric moments also have numerous applications for climate studies, for example, quality improvement of reflectivity and rain rate retrievals, and for interrogating microphysical and dynamical processes. However, observational data alone are not useful if they are not accompanied by sufficient metadata. Since the lifetime of a radar ranges between 10 and 20 years, instruments are typically replaced or upgraded during climatologically relevant time periods. As a result, present metadata often do not apply to past data. This paper outlines the work of the Radar Task Team set by the Atmospheric Observation Panel for Climate (AOPC) and summarizes results from a recent survey on the existence and availability of long time series. We also provide recommendations for archiving current and future data and examples of climatological studies in which radar data have already been used.
Quantitative precipitation nowcasting (QPN) has become an
essential technique in various application contexts, such as early warning or
urban sewage control. A common heuristic prediction approach is ...to track the
motion of precipitation features from a sequence of weather radar images and
then to displace the precipitation field to the imminent future (minutes to
hours) based on that motion, assuming that the intensity of the features
remains constant (“Lagrangian persistence”). In that context, “optical
flow” has become one of the most popular tracking techniques. Yet the
present landscape of computational QPN models still struggles with producing
open software implementations. Focusing on this gap, we have developed and
extensively benchmarked a stack of models based on different optical flow
algorithms for the tracking step and a set of parsimonious extrapolation
procedures based on image warping and advection. We demonstrate that these
models provide skillful predictions comparable with or even superior to
state-of-the-art operational software. Our software library (“rainymotion”)
for precipitation nowcasting is written in the Python programming language
and openly available at GitHub (https://github.com/hydrogo/rainymotion,
Ayzel et al., 2019). That way,
the library may serve as a tool for providing fast, free, and transparent
solutions that could serve as a benchmark for further model development and
hypothesis testing – a benchmark that is far more advanced than the
conventional benchmark of Eulerian persistence commonly used in QPN
verification experiments.
Weather radar rainfall data in urban hydrology Thorndahl, Søren; Einfalt, Thomas; Willems, Patrick ...
Hydrology and earth system sciences,
03/2017, Letnik:
21, Številka:
3
Journal Article
Recenzirano
Odprti dostop
Application of weather radar data in urban hydrological applications has evolved significantly during the past decade as an alternative to traditional rainfall observations with rain gauges. Advances ...in radar hardware, data processing, numerical models, and emerging fields within urban hydrology necessitate an updated review of the state of the art in such radar rainfall data and applications. Three key areas with significant advances over the past decade have been identified: (1) temporal and spatial resolution of rainfall data required for different types of hydrological applications, (2) rainfall estimation, radar data adjustment and data quality, and (3) nowcasting of radar rainfall and real-time applications. Based on these three fields of research, the paper provides recommendations based on an updated overview of shortcomings, gains, and novel developments in relation to urban hydrological applications. The paper also reviews how the focus in urban hydrology research has shifted over the last decade to fields such as climate change impacts, resilience of urban areas to hydrological extremes, and online prediction/warning systems. It is discussed how radar rainfall data can add value to the aforementioned emerging fields in current and future applications, but also to the analysis of integrated water systems.
Weather radar has become an invaluable tool for monitoring rainfall and studying its link to hydrological response. However, when it comes to accurately measuring small-scale rainfall extremes ...responsible for urban flooding, many challenges remain. The most important of them is that radar tends to underestimate rainfall compared to gauges. The hope is that by measuring at higher resolutions and making use of dual-polarization radar, these mismatches can be reduced. Each country has developed its own strategy for addressing this issue. However, since there is no common benchmark, improvements are hard to quantify objectively. This study sheds new light on current performances by conducting a multinational assessment of radar's ability to capture heavy rain events at scales of 5 min up to 2 h. The work is performed within the context of the joint experiment framework of project MUFFIN (Multiscale Urban Flood Forecasting), which aims at better understanding the link between rainfall and urban pluvial flooding across scales. In total, six different radar products in Denmark, the Netherlands, Finland and Sweden were considered. The top 50 events in a 10-year database of radar data were used to quantify the overall agreement between radar and gauges as well as the bias affecting the peaks. Results show that the overall agreement in heavy rain is fair (correlation coefficient 0.7–0.9), with apparent multiplicative biases on the order of 1.2–1.8 (17 %–44 % underestimation). However, after taking into account the different sampling volumes of radar and gauges, actual biases could be as low as 10 %. Differences in sampling volumes between radar and gauges play an important role in explaining the bias but are hard to quantify precisely due to the many post-processing steps applied to radar. Despite being adjusted for bias by gauges, five out of six radar products still exhibited a clear conditional bias, with intensities of about 1 %–2 % per mmh−1. As a result, peak rainfall intensities were severely underestimated (factor 1.8–3.0 or 44 %–67 %). The most likely reason for this is the use of a fixed Z–R relationship when estimating rainfall rates (R) from reflectivity (Z), which fails to account for natural variations in raindrop size distribution with intensity. Based on our findings, the easiest way to mitigate the bias in times of heavy rain is to perform frequent (e.g., hourly) bias adjustments with the help of rain gauges, as demonstrated by the Dutch C-band product. An even more promising strategy that does not require any gauge adjustments is to estimate rainfall rates using a combination of reflectivity (Z) and differential phase shift (Kdp), as done in the Finnish OSAPOL product. Both approaches lead to approximately similar performances, with an average bias (at 10 min resolution) of about 30 % and a peak intensity bias of about 45 %.
Abstract Over the years, research has been directed to investigate radar as potential tools for estimating rain. The radar reflectivity is measured by comparing the location of the rain gauge and the ...associated vertical path at certain altitude. The latitude and longitude of the rain gauge is identified to pinpoint the exact associated location by using Rainbow®5 meteorological software. In this work, radar reflectivity was estimated by using S-band Terminal Doppler Weather Radar (TWDR) data at Malaysian KLIA radar station. The rain gauge is owned by Malaysian Department of Irrigation and Drainage (DID) at several locations within the vicinity of the radar. A technique to estimate the Z-R relationship by using radar reflectivity information and rainfall rate from rain gauge values is demonstrated in this article. Comparisons between radar reflectivity and actual rain gauge measurement is presented. Preliminary findings using available Z-R relation shows similar values with the actual measurement and suitable to be used in tropical climate country such as Malaysia.
Precipitation nowcasting pertains to the localized forecasting of rainfall over a brief time horizon, characterized by precise estimates of both coverage and intensity. This capability holds ...particular significance in various societal applications, including agriculture, aviation safety, and transportation. However, since traditional methods mainly by extrapolating radar echo in two-dimensional space, cannot accurately and sufficiently represent the spatiotemporal state of clouds in the vertical direction, the accuracy of precipitation nowcasting using weather radar has reached a bottleneck. A new deep learning precipitation nowcasting model called 3dCloudNet is designed and evaluated in this study. The 3dCloudNet incorporates historical three-dimensional radar echo sequences obtained from weather radar data to improve the accuracy and reliability of precipitation nowcasting. By capturing both horizontal and vertical motion patterns of clouds at various altitude levels, this model demonstrates an enhanced capability in detecting and distinguishing regions prone to severe convective weather events. The experimental results show that the model better captures clouds motion patterns and trends, and therefore has a noteworthy ability to detect and distinguish areas that may lead to severe convective weather. This study provides a step towards further improving the accuracy of precipitation nowcasting.
Quantitative precipitation estimation with commercial microwave links (CMLs) is a technique developed to supplement weather radar and rain gauge observations. It is exploiting the relation between ...the attenuation of CML signal levels and the integrated rain rate along a CML path. The opportunistic nature of this method requires a sophisticated data processing using robust methods. In this study we focus on the processing step of rain event detection in the signal level time series of the CMLs, which we treat as a binary classification problem. This processing step is particularly challenging, because even when there is no rain, the signal level can show large fluctuations similar to that during rainy periods. False classifications can have a high impact on falsely estimated rainfall amounts. We analyze the performance of a convolutional neural network (CNN), which is trained to detect rainfall-specific attenuation patterns in CML signal levels, using data from 3904 CMLs in Germany. The CNN consists of a feature extraction and a classification part with, in total, 20 layers of neurons and 1.4×105 trainable parameters. With a structure inspired by the visual cortex of mammals, CNNs use local connections of neurons to recognize patterns independent of their location in the time series. We test the CNN's ability to recognize attenuation patterns from CMLs and time periods outside the training data. Our CNN is trained on 4 months of data from 800 randomly selected CMLs and validated on 2 different months of data, once for all CMLs and once for the 3104 CMLs not included in the training. No CMLs are excluded from the analysis. As a reference data set, we use the gauge-adjusted radar product RADOLAN-RW provided by the German meteorological service (DWD). The model predictions and the reference data are compared on an hourly basis. Model performance is compared to a state-of-the-art reference method, which uses the rolling standard deviation of the CML signal level time series as a detection criteria. Our results show that within the analyzed period of April to September 2018, the CNN generalizes well to the validation CMLs and time periods. A receiver operating characteristic (ROC) analysis shows that the CNN is outperforming the reference method, detecting on average 76 % of all rainy and 97 % of all nonrainy periods. From all periods with a reference rain rate larger than 0.6 mm h−1, more than 90 % was detected. We also show that the improved event detection leads to a significant reduction of falsely estimated rainfall by up to 51 %. At the same time, the quality of the correctly estimated rainfall is kept at the same level in regards to the Pearson correlation with the radar rainfall. In conclusion, we find that CNNs are a robust and promising tool to detect rainfall-induced attenuation patterns in CML signal levels from a large CML data set covering all of Germany.
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