A new module for the tracking of radar-derived precipitation with model-derived winds

A new approach for the nowcasting of precipitation has been developed at the German Weather Service combining extrapolation techniques and Numerical Weather Prediction (NWP) for a lead time range of several hours. Radar-derived precipitation fields serve as input data for a tracking algorithm using model-derived wind data. The composite precipitation field is derived from the precipitation scans which are performed every five minutes at the 16 German radar stations. The data are corrected from clutter and shading effects. The tracking of this radar-derived precipitation field is performed using the temporally and spatially resolved horizontal wind fields at different pressure levels provided by the Local Model Europe (LME). The optimal wind field is derived from minimization of the least-squares difference between a linear combination of model wind data from different pressure levels and the linear displacement vectors calculated via pattern recognition from previous radar measurements. An area-preserving displacement of the precipitation fields is realized by eliminating the wind field divergence and by omitting the dynamical evolution of the precipitation fields. Advection is performed using the fourth-order Bott scheme. Forecasted data comprise precipitation rates for every five minutes lead time as well as hourly sums of precipitation. The verification of a case study's results against radar precipitation measurements lead to a mean Equitable Threat Score (ETS) of 70%, 46%, and 38% for the first, second, and third forecast hour, respectively

Erstellung einer radargestützten Niederschlagsklimatologie

Es ist Konsens in der Klimaforschungsgemeinschaft, dass der globale Klimawandel mit großer Wahrscheinlichkeit mit einer erhöhten Häufigkeit von witterungsbedingten Naturkatastrophen einhergeht [IPCC, 2011]. Grundlage für diese Schlussfolgerung sind im Wesentlichen die Ergebnisse globaler und regionaler Klimasimulationen. Neben Windstürmen sind insbesondere Auftreten und Häufigkeit von hydrometeorologischen Extremereignissen wie z. B. Starkregen oder Dürre ursächlich für korrespondierende Naturkatastrophen, die für den Katastrophenschutz relevant sind. So sind die Anzahl von Umwelteinsätzen der Feuerwehr im Nachgang von Extremereignissen wie Starkregen oder Hagel [Geier, 2009] sowie die Zahl der wetterbedingten Einsätze des Technischen Hilfswerks in den vergangenen Jahren gestiegen [Strotmann, 2011]. Dieser Trend wird von einer Befragung der im Katastrophenschutz eingebundenen Organisationen bestätigt, nach deren Ergebnissen neben Sturmereignissen vor allem Hochwasser vermehrt Einsätze nach sich ziehen

Modeling of biomass smoke injection into the lower stratosphere by a large forest fire (Part I): reference simulation

Wildland fires in boreal regions have the potential to initiate deep convection, so-called pyro-convection, due to their release of sensible heat. Under favorable atmospheric conditions, large fires can result in pyro-convection that transports the emissions into the upper troposphere and the lower stratosphere. Here, we present three-dimensional model simulations of the injection of fire emissions into the lower stratosphere by pyro-convection. These model simulations are constrained and evaluated with observations obtained from the Chisholm fire in Alberta, Canada, in 2001. The active tracer high resolution atmospheric model (ATHAM) is initialized with observations obtained by radiosonde. Information on the fire forcing is obtained from ground-based observations of the mass and moisture of the burned fuel. Based on radar observations, the pyro-convection reached an altitude of about 13 km, well above the tropopause, which was located at about 11.2 km. The model simulation yields a similarly strong convection with an overshoot of the convection above the tropopause. The main outflow from the pyro-convection occurs at about 10.6 km, but a significant fraction (about 8%) of the emitted mass of the smoke aerosol is transported above the tropopause. In contrast to regular convection, the region with maximum updraft velocity in the pyro-convection is located close to the surface above the fire. This results in high updraft velocities >10 m s^−1 at cloud base. The temperature anomaly in the plume decreases rapidly with height from values above 50 K at the fire to about 5 K at about 3000 m above the fire. While the sensible heat released from the fire is responsible for the initiation of convection in the model, the release of latent heat from condensation and freezing dominates the overall energy budget. Emissions of water vapor from the fire do not significantly contribute to the energy budget of the convection

Challenges in quantifying changes in the global water cycle

Human influences have likely already impacted the large-scale water cycle but natural variability and observational uncertainty are substantial. It is essential to maintain and improve observational capabilities to better characterize changes. Understanding observed changes to the global water cycle is key to predicting future climate changes and their impacts. While many datasets document crucial variables such as precipitation, ocean salinity, runoff, and humidity, most are uncertain for determining long-term changes. In situ networks provide long time-series over land but are sparse in many regions, particularly the tropics. Satellite and reanalysis datasets provide global coverage, but their long-term stability is lacking. However, comparisons of changes among related variables can give insights into the robustness of observed changes. For example, ocean salinity, interpreted with an understanding of ocean processes, can help cross-validate precipitation. Observational evidence for human influences on the water cycle is emerging, but uncertainties resulting from internal variability and observational errors are too large to determine whether the observed and simulated changes are consistent. Improvements to the in situ and satellite observing networks that monitor the changing water cycle are required, yet continued data coverage is threatened by funding reductions. Uncertainty both in the role of anthropogenic aerosols, and due to large climate variability presently limits confidence in attribution of observed changes

Rain erosivity map for Germany derived from contiguous radar rain data

Erosive rainfall varies pronouncedly in time and space. Severe events are often restricted to a few square kilometers. Radar rain data with high spatiotemporal resolution enable this pattern of erosivity to be portrayed with high detail. We used radar data with a spatial resolution of 1 km"span class="inline-formula"""sup"2"/sup""/span" over 452 503 km"span class="inline-formula"""sup"2"/sup""/span" to derive a new erosivity map for Germany and to analyze the seasonal distribution of erosivity. The expected long-term regional pattern was extracted from the scattered pattern of events by several steps of smoothing. This included averaging erosivity from 2001 to 2017 and smoothing in time and space. The pattern of the resulting map was predominantly shaped by orography. It generally agrees well with the erosivity map currently used in Germany (Sauerborn map), which is based on regressions using rain gauge data (mainly from the 1960s to 1980s). In some regions the patterns of both maps deviate because the regressions of the Sauerborn map were weak. Most importantly, the new map shows that erosivity is about 66 % larger than in the Sauerborn map. This increase in erosivity was confirmed by long-term data from rain gauge stations that were used for the Sauerborn map and which are still in operation. The change was thus not caused by using a different methodology but by climate change since the 1970s. Furthermore, the seasonal distribution of erosivity shows a slight shift towards the winter period when soil cover by plants is usually poor. This shift in addition to the increase in erosivity may have caused an increase in erosion for many crops. For example, predicted soil erosion for winter wheat is now about 4 times larger than in the 1970s. These highly resolved topical erosivity data will thus have definite consequences for agricultural advisory services, landscape planning and even political decisions. Document type: Articl

Optical flow models as an open benchmark for radar-based precipitation nowcasting (rainymotion v0.1)

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.</p

An Analysis of the Chemical Processes in the Smoke Plume from a Savanna Fire

[1] Photochemistry in young plumes from vegetation fires significantly transforms the initial fire emissions within the first hour after the emissions are injected into the atmosphere. Here we present an investigation of field measurements obtained in a smoke plume from a prescribed savanna fire during the SAFARI 2000 field experiment using a detailed photochemical box-dilution model. The dilution used in the model simulations was constrained by measurements of chemically passive tracers (e.g., CO) near and downwind of the fire. The emissions of the dominant carbonaceous compounds, including oxygenated ones, were taken into account. The field measurements revealed significant production of ozone and acetic acid in the gas phase. The photochemical model simulations also predict ozone production, but significantly less than the measurements. The underestimation of the ozone production in the model simulations is likely caused by shortcomings of our current understanding of ozone photochemistry under the polluted conditions in this young smoke plume. Several potential reasons for this discrepancy are discussed. One possible cause could be the neglect of unmeasured emissions or surface reactions of NO2 with methanol or other hydrocarbons. In contrast to the field measurements, no significant production of acetic acid was simulated by the model. We know of no gas-phase reactions that cause the production of acetic acid on the timescale considered here. Though many processes were well-simulated by the model, there is a need for further research on some key photochemical processes within young plumes from biomass burning and the potential interactions between gas and the particulate phases. These fundamental photochemical processes may also be of importance in other polluted environments

Temporal- and spatial-scale and positional effects on rain erosivity derived from point-scale and contiguous rain data

Up until now, erosivity required for soil loss predictions has been mainly estimated from rain gauge data at point scale and then spatially interpolated to erosivity maps. Contiguous rain data from weather radar measurements, satellites, cellular communication networks and other sources are now available, but they differ in measurement method and temporal and spatial scale from data at point scale. We determined how the intensity threshold of erosive rains has to be modified and which scaling factors have to be applied to account for the differences in method and scales. Furthermore, a positional effect quantifies heterogeneity of erosivity within 1&thinsp;km2, which presently is the highest resolution of freely available gauge-adjusted radar rain data. These effects were analysed using several large data sets with a total of approximately 2×106 erosive events (e.g. records of 115 rain gauges for 16 years distributed across Germany and radar rain data for the same locations and events). With decreasing temporal resolution, peak intensities decreased and the intensity threshold was met less often. This became especially pronounced when time increments became larger than 30&thinsp;min. With decreasing spatial resolution, intensity peaks were also reduced because additionally large areas without erosive rain were included within one pixel. This was due to the steep spatial gradients in erosivity. Erosivity of single events could be zero or more than twice the mean annual sum within a distance of less than 1&thinsp;km. We conclude that the resulting large positional effect requires use of contiguous rain data, even over distances of less than 1&thinsp;km, but at the same time contiguously measured radar data cannot be resolved to point scale. The temporal scale is easier to consider, but with time increments larger than 30&thinsp;min the loss of information increases considerably. We provide functions to account for temporal scale (from 1 to 120&thinsp;min) and spatial scale (from rain gauge to pixels of 18&thinsp;km width) that can be applied to rain gauge data of low temporal resolution and to contiguous rain data.</p

Rain erosivity map for Germany derived from contiguous radar rain data

Erosive rainfall varies pronouncedly in time and space. Severe events are often restricted to a few square kilometers. Radar rain data with high spatiotemporal resolution enable this pattern of erosivity to be portrayed with high detail. We used radar data with a spatial resolution of 1&thinsp;km2 over 452&thinsp;503&thinsp;km2 to derive a new erosivity map for Germany and to analyze the seasonal distribution of erosivity. The expected long-term regional pattern was extracted from the scattered pattern of events by several steps of smoothing. This included averaging erosivity from 2001 to 2017 and smoothing in time and space. The pattern of the resulting map was predominantly shaped by orography. It generally agrees well with the erosivity map currently used in Germany (Sauerborn map), which is based on regressions using rain gauge data (mainly from the 1960s to 1980s). In some regions the patterns of both maps deviate because the regressions of the Sauerborn map were weak. Most importantly, the new map shows that erosivity is about 66&thinsp;% larger than in the Sauerborn map. This increase in erosivity was confirmed by long-term data from rain gauge stations that were used for the Sauerborn map and which are still in operation. The change was thus not caused by using a different methodology but by climate change since the 1970s. Furthermore, the seasonal distribution of erosivity shows a slight shift towards the winter period when soil cover by plants is usually poor. This shift in addition to the increase in erosivity may have caused an increase in erosion for many crops. For example, predicted soil erosion for winter wheat is now about 4 times larger than in the 1970s. These highly resolved topical erosivity data will thus have definite consequences for agricultural advisory services, landscape planning and even political decisions.</p