scholarly journals A Real-Time Evaporation Correction Scheme for Radar-Derived Mosaicked Precipitation Estimations

2018 ◽  
Vol 19 (1) ◽  
pp. 87-111 ◽  
Author(s):  
Steven M. Martinaitis ◽  
Heather M. Grams ◽  
Carrie Langston ◽  
Jian Zhang ◽  
Kenneth Howard
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Weather Prediction ◽  
Horizontal Resolution ◽  
Surface Precipitation ◽  
Correction Scheme ◽  
Evaporation Technique ◽  
Microphysical Processes ◽  
Rain Events ◽  
2D And 3D

Abstract Precipitation values estimated by radar are assumed to be the amount of precipitation that occurred at the surface, yet this notion is inaccurate. Numerous atmospheric and microphysical processes can alter the precipitation rate between the radar beam elevation and the surface. One such process is evaporation. This study determines the applicability of integrating an evaporation correction scheme for real-time radar-derived mosaicked precipitation rates to reduce quantitative precipitation estimate (QPE) overestimation and to reduce the coverage of false surface precipitation. An evaporation technique previously developed for large-scale numerical modeling is applied to Multi-Radar Multi-Sensor (MRMS) precipitation rates through the use of 2D and 3D numerical weather prediction (NWP) atmospheric parameters as well as basic radar properties. Hourly accumulated QPE with evaporation adjustment compared against gauge observations saw an average reduction of the overestimation bias by 57%–76% for rain events and 42%–49% for primarily snow events. The removal of false surface precipitation also reduced the number of hourly gauge observations that were considered as “false zero” observations by 52.1% for rain and 38.2% for snow. Optimum computational efficiency was achieved through the use of simplified equations and hourly 10-km horizontal resolution NWP data. The run time for the evaporation correction algorithm is 6–7 s.

scholarly journals Effects of Liquid Phase Cloud Microphysical Processes in Mixed Phase Cumulus Clouds over the Tibetan Plateau

2019 ◽  
Author(s):  
Xiaoqi Xu ◽  
Chunsong Lu ◽  
Yangang Liu ◽  
Wenhua Gao ◽  
Yuan Wang ◽  
...  
Keyword(s):  
Liquid Phase ◽  
Tibetan Plateau ◽  
Large Scale ◽  
Precipitation Event ◽  
The Tibetan Plateau ◽  
Phase Precipitation ◽  
Physical Reason ◽  
Surface Precipitation ◽  
Skill Scores ◽  
Microphysical Processes

Abstract. Overprediction of precipitation over the Tibetan Plateau is often found in numerical simulations, which is thought to be related to coarse grid sizes or inaccurate large-scale forcing. In addition to confirming the important role of model grid sizes, this study shows that liquid-phase precipitation parameterization is another key culprit, and underlying physical mechanisms are revealed. A typical summer plateau precipitation event is simulated with the Weather Research and Forecasting (WRF) model by introducing different parameterizations of liquid-phase microphysical processes into the commonly used Morrison scheme, including autoconversion, accretion, and entrainment-mixing mechanisms. All simulations can reproduce the general spatial distribution and temporal variation of precipitation. The precipitation in the high-resolution domain is less overpredicted than in the low-resolution domain. The accretion process plays more important roles than other liquid-phase processes in simulating precipitation. Employing the accretion parameterization considering raindrop size makes the total surface precipitation closest to the observation which is supported by the Heidke skill scores. The physical reason is that this accretion parameterization can suppress fake accretion and liquid-phase precipitation when cloud droplets are too small to initiate precipitation.

Global climate simulations at 2.8 km on GPU with the ICON model

2020 ◽  
Author(s):  
Xavier Lapillonne ◽  
William Sawyer ◽  
Philippe Marti ◽  
Valentin Clement ◽  
Remo Dietlicher ◽  
...  
Keyword(s):  
High Resolution ◽  
Large Scale ◽  
Climate Model ◽  
Global Climate ◽  
Weather Prediction ◽  
Time Integration ◽  
Horizontal Resolution ◽  
Performance Comparison ◽  
Quasi Biennial Oscillation ◽  
Numerical Weather

<p>The ICON modelling framework is a unified numerical weather and climate model used for applications ranging from operational numerical weather prediction to low and high resolution climate projection. In view of further pushing the frontier of possible applications and to make use of the latest evolution in hardware technologies, parts of the model were recently adapted to run on heterogeneous GPU system. This initial GPU port focus on components required for high-resolution climate application, and allow considering multi-years simulations at 2.8 km on the Piz Daint heterogeneous supercomputer. These simulations are planned as part of the QUIBICC project “The Quasi-Biennial Oscillation (QBO) in a changing climate”, which propose to investigate effects of climate change on the dynamics of the QBO.</p><p>Because of the low compute intensity of atmospheric model the cost of data transfer between CPU and GPU at every step of the time integration would be prohibitive if only some components would be ported to the accelerator. We therefore present a full port strategy where all components required for the simulations are running on the GPU. For the dynamics, most of the physical parameterizations and infrastructure code the OpenACC compiler directives are used. For the soil parameterization, a Fortran based domain specific language (DSL) the CLAW-DSL has been considered. We discuss the challenges associated to port a large community code, about 1 million lines of code, as well as to run simulations on large-scale system at 2.8 km horizontal resolution in terms of run time and I/O constraints. We show performance comparison of the full model on CPU and GPU, achieving a speed up factor of approximately 5x, as well as scaling results on up to 2000 GPU nodes. Finally we discuss challenges and planned development regarding performance portability and high level DSL which will be used with the ICON model in the near future.</p>

scholarly journals 28 Shifting to Virtual MDT in the COVID-19 Era and Beyond

2021 ◽  
Vol 108 (Supplement_2) ◽  
Author(s):  
D Vijayan ◽  
K Malik ◽  
K Natarajan ◽  
J Berland ◽  
D Morton ◽  
...  
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Mixed Reality ◽  
Clinician Training ◽  
Smart Glasses ◽  
Digital Network ◽  
Image Characteristics ◽  
Microsoft Hololens ◽  
2D And 3D ◽  
Virtual Ecosystem

Abstract Aim The COVID19 pandemic has accelerated the need for staff to work remotely. Our aim was to demonstrate how a next-generation digital platform could be used to create a virtual MDT ecosystem in order to manipulate holographic 2D and 3D images in real-time. Method This study involved setting up a mock virtual MDT using de-identified DICOM files from a patient who had been treated for colorectal cancer and then subsequently found to have a liver metastasis. The image file was segmented and converted into a 2D and 3D format for visualisation within Microsoft HoloLens 2 ® (smart glasses) using Holocare Solutions ® (Mixed Reality software). Results A seamless cross-border pipeline was developed that involved "clinician" training, DICOM segmentation and virtual connection. We successfully performed a virtual MDT with participants able to visualise and manipulate a virtual 3D organ in real-time. The digital network remotely connected sites in England and Norway. The streaming quality was stable and HIPAA compliant. Each participant could observe others as "avatars" interacting with images within the virtual ecosystem allowing image characteristics to be highlighted. Conclusions We successfully conducted a virtual MDT using novel hardware and software. Our intention is to conduct a large-scale study to assess the platform's effectiveness in “Real World" MDTs.

scholarly journals Evaluation of the COSMO model (v5.1) in polarimetric radar space – Impact of uncertainties in model microphysics, retrievals, and forward operator

2021 ◽  
Author(s):  
Prabhakar Shrestha ◽  
Jana Mendrok ◽  
Velibor Pejcic ◽  
Silke Trömel ◽  
Ulrich Blahak ◽  
...  
Keyword(s):  
Weather Prediction ◽  
Model Space ◽  
Polarimetric Radar ◽  
Surface Precipitation ◽  
The Difference ◽  
Microphysical Processes ◽  
Nwp Model ◽  
Two Parameters ◽  
Ice Water ◽  
Forward Operator

Abstract. Sensitivity experiments with a numerical weather prediction (NWP) model and polarimetric radar forward operator (FO) are conducted for a long-duration stratiform event over northwestern Germany, to evaluate uncertainties in the partitioning of the ice water content and assumptions of hydrometeor scattering properties in the NWP model and FO, respectively. Polarimetric observations from X-band radar and retrievals of hydrometeor classifications are used for comparison with the multiple experiments in radar and model space. Modifying two parameters (Dice and Tgr) responsible for the production of snow and graupel, respectively, was found to improve the synthetic polarimetric moments and simulated hydrometeor population, while keeping the difference in surface precipitation statistically insignificant at model resolvable grid scales. However, the model still exhibited a low bias in simulated polarimetric moments at lower levels above the melting layer (−3 to −13 °C) where snow was found to dominate. This necessitates further research into the missing microphysical processes in these lower levels (e.g., fragmentation due to ice-ice collisions), and use of more reliable snow scattering models to draw valid conclusions.

scholarly journals Evaluation of the COSMO model (v5.1) in polarimetric radar space – impact of uncertainties in model microphysics, retrievals and forward operators

2022 ◽  
Vol 15 (1) ◽  
pp. 291-313
Author(s):  
Prabhakar Shrestha ◽  
Jana Mendrok ◽  
Velibor Pejcic ◽  
Silke Trömel ◽  
Ulrich Blahak ◽  
...  
Keyword(s):  
Weather Prediction ◽  
Critical Diameter ◽  
Model Space ◽  
Threshold Temperature ◽  
Polarimetric Radar ◽  
Surface Precipitation ◽  
The Difference ◽  
Microphysical Processes ◽  
Nwp Model ◽  
Lower Magnitude

Abstract. Sensitivity experiments with a numerical weather prediction (NWP) model and polarimetric radar forward operator (FO) are conducted for a long-duration stratiform event over northwestern Germany to evaluate uncertainties in the partitioning of the ice water content and assumptions of hydrometeor scattering properties in the NWP model and FO, respectively. Polarimetric observations from X-band radar and retrievals of hydrometeor classifications are used for comparison with the multiple experiments in radar and model space. Modifying the critical diameter of particles for ice-to-snow conversion by aggregation (Dice) and the threshold temperature responsible for graupel production by riming (Tgr), was found to improve the synthetic polarimetric moments and simulated hydrometeor population, while keeping the difference in surface precipitation statistically insignificant at model resolvable grid scales. However, the model still exhibited a low bias (lower magnitude than observation) in simulated polarimetric moments at lower levels above the melting layer (−3 to −13 ∘C) where snow was found to dominate. This necessitates further research into the missing microphysical processes in these lower levels (e.g. fragmentation due to ice–ice collisions) and use of more reliable snow-scattering models to draw valid conclusions.

scholarly journals HydroGFD3.0: a 25 km global near real-time updated precipitation and temperature data set

2020 ◽  
Author(s):  
Peter Berg ◽  
Fredrik Almén ◽  
Denica Bozhinova
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Hydrological Modeling ◽  
Horizontal Resolution ◽  
Maximum Temperature ◽  
Historical Period ◽  
Data Sets ◽  
Monthly Precipitation ◽  
Time Data ◽  
Data Set

Abstract. HydroGFD (Hydrological Global Forcing Data) is a data set of bias adjusted reanalysis data for daily precipitation, and minimum, mean, and maximum temperature. It is mainly intended for large scale hydrological modeling, but is also suitable for other impact modeling. The data set has an almost global land area coverage, excluding the Antarctic continent, at a horizontal resolution of 0.25°, i.e. about 25 km. It is available for the complete ERA5 reanalysis time period; currently 1979 until five days ago. This period will be extended back to 1950 once the back catalogue of ERA5 is available. The historical period is adjusted using global gridded observational data sets, and to acquire real-time data, a collection of several reference data sets is used. Consistency in time is attempted by relying on a background climatology, and only making use of anomalies from the different data sets. Precipitation is adjusted for mean bias as well as the number or wet days in a month. The latter is relying on a calibrated statistical method with input only of the monthly precipitation anomaly, such that no additional input data about the number of wet days is necessary. The daily mean temperature is adjusted toward the monthly mean of the observations, and applied to 1 h timesteps of the ERA5 reanalysis. Daily mean, minimum and maximum temperature are then calculated. The performance of the HydroGFD3 data set is on par with other similar products, although there are significant differences in different parts of the globe, especially where observations are uncertain. Further, HydroGFD3 tends to have higher precipitation extremes, partly due to its higher spatial resolution. In this paper, we present the methodology, evaluation results, and how to access to the data set at https://doi.org/10.5281/zenodo.3871707.

scholarly journals Hydrometeorological aspects of the Real-Time Ultrafinescale Forecast Support during the Special Observing Period of the MAP<sup>*</sup>

2003 ◽  
Vol 7 (6) ◽  
pp. 877-889 ◽  
Author(s):  
R. Benoit ◽  
N. Kouwen ◽  
W. Yu ◽  
S. Chamberland ◽  
P. Pellerin
Keyword(s):  
Real Time ◽  
Hydrological Model ◽  
Weather Prediction ◽  
Cloud Microphysics ◽  
Horizontal Resolution ◽  
Border Region ◽  
Computational Domain ◽  
Microphysics Scheme ◽  
The Real ◽  
Numerical Weather

Abstract. During the Special Observation Period (SOP, 7 September–15 November, 1999) of the Mesoscale Alpine Programme (MAP), the Canadian Mesoscale Compressible Community Model (MC2) was run in real time at a horizontal resolution of 3 km on a computational domain of 350☓300☓50 grid points, covering the whole of the Alpine region. The WATFLOOD model was passively coupled to the MC2; the former is an integrated set of computer programs to forecast flood flows, using all available data, for catchments with response times ranging from one hour to several weeks. The unique aspect of this contribution is the operational application of numerical weather prediction data to forecast flows over a very large, multinational domain. An overview of the system performance from the hydrometeorological aspect is presented, mostly for the real-time results, but also from subsequent analyses. A streamflow validation of the precipitation is included for large basins covering upper parts of the Rhine and the Rhone, and parts of the Po and of the Danube. In general, the MC2/WATFLOOD model underestimated the total runoff because of the under-prediction of precipitation by MC2 during the MAP SOP. After the field experiment, a coding error in the cloud microphysics scheme of MC2 explains this underestimation to a large extent. A sensitivity study revealed that the simulated flows reproduce the major features of the observed flow record for most of the flow stations. The experiment was considered successful because two out of three possible flood events in the Swiss-Italian border region were predicted correctly by data from the numerical weather models linked to the hydrological model and no flow events were missed. This study has demonstrated that a flow forecast from a coupled atmospheric-hydrological model can serve as a useful first alert and quantitative forecast. Keywords: mesoscale atmospheric model, hydrological model, flood forecasting, Alps

scholarly journals HydroGFD3.0 (Hydrological Global Forcing Data): a 25 km global precipitation and temperature data set updated in near-real time

2021 ◽  
Vol 13 (4) ◽  
pp. 1531-1545
Author(s):  
Peter Berg ◽  
Fredrik Almén ◽  
Denica Bozhinova
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Horizontal Resolution ◽  
Precipitation Anomaly ◽  
Maximum Temperature ◽  
Historical Period ◽  
Data Sets ◽  
Monthly Precipitation ◽  
Time Data ◽  
Data Set

Abstract. HydroGFD3 (Hydrological Global Forcing Data) is a data set of bias-adjusted reanalysis data for daily precipitation and minimum, mean, and maximum temperature. It is mainly intended for large-scale hydrological modelling but is also suitable for other impact modelling. The data set has an almost global land area coverage, excluding the Antarctic continent and small islands, at a horizontal resolution of 0.25∘, i.e. about 25 km. It is available for the complete ERA5 reanalysis time period, currently 1979 until 5 d ago. This period will be extended back to 1950 once the back catalogue of ERA5 is available. The historical period is adjusted using global gridded observational data sets, and to acquire real-time data, a collection of several reference data sets is used. Consistency in time is attempted by relying on a background climatology and only making use of anomalies from the different data sets. Precipitation is adjusted for mean bias as well as the number of wet days in a month. The latter is relying on a calibrated statistical method with input only of the monthly precipitation anomaly such that no additional input data about the number of wet days are necessary. The daily mean temperature is adjusted toward the monthly mean of the observations and applied to 1 h time steps of the ERA5 reanalysis. Daily mean, minimum, and maximum temperature are then calculated. The performance of the HydroGFD3 data set is on par with other similar products, although there are significant differences in different parts of the globe, especially where observations are uncertain. Further, HydroGFD3 tends to have higher precipitation extremes, partly due to its higher spatial resolution. In this paper, we present the methodology, evaluation results, and how to access the data set at https://doi.org/10.5281/zenodo.3871707 (Berg et al., 2020).

Real Time Rainfall Monitoring for Pipeline Geohazards

2021 ◽  
Author(s):  
Gerry Ferris ◽  
Patrick Grover ◽  
Aron Zahradka
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Oil And Gas ◽  
Weather Prediction ◽  
Extreme Rainfall ◽  
Rainfall Data ◽  
Annual Rainfall ◽  
Extreme Rainfall Event ◽  
Pipeline Failures

Abstract Oil and gas pipelines are subjected to multiple types of geohazards which cause pipeline failures (loss of containment); two of the most common types occur at watercourse crossings and at landslides. At watercourse crossings, the most common geohazard which causes pipeline failures is flooding during which excessive scour may result in the exposure of the buried pipeline and if the exposure results in a free spanning pipeline, then this may fail due to fatigue caused by cyclic loading from vortex-induced vibration. Fortunately the free span length and water velocity combinations that lead to failure can be defined and can be used to identify the flood discharge that should be monitored for in order to trigger actions to manage the hazard and avoid failure. Most watercourse crossings in a pipeline network are on ungauged watercourses and necessitate the use of a proxy gauged watercourse. The “proxy” gauged watercourse is used to infer whether flooding is occurring on the ungauged crossing, and the owner can take appropriate actions. Often the proxy gauged watercourse is too far away or the watercourse may not be representative of the crossing of concern (e.g. large difference in the drainage areas). Real-time rainfall data can be used in conjunction with streamflow monitoring to determine when extreme precipitation has occurred within the ungauged watercourses catchment which may result in flooding. Where pipelines cross landslide prone areas, large scale movements can be initiated, or slow on-going movement rates increased when extreme rainfall occurs. The definition of the extreme rainfall event for slope sites is the key component of providing a suitable warning of potentially dangerous conditions; shallow slides can be caused by short term events from sub-hourly to 3 day duration precipitation events whereas large deep seated (creeping) landslides can be driven by annual and intra-annual rainfall amounts. Monitoring of real time rainfall can be used to determine when extreme rainfall occurs at a landslide site. The density of in-situ weather stations collecting real-time rainfall data prevents the application along remote sections of pipeline routes and within large sections of Canada. Gridded real time rainfall from quantitative precipitation estimations which integrate a multiple data sources including in-situ, numerical weather prediction, satellite and weather radar, can be used to overcome this problem and provide warnings when pre-determined rainfall thresholds are exceeded on a site-specific basis.

scholarly journals Adaptation of the Predicted Particles Properties (P3) Microphysics Scheme for Large-Scale Numerical Weather Prediction

2020 ◽  
Vol 35 (6) ◽  
pp. 2541-2565
Author(s):  
Caroline Jouan ◽  
Jason A. Milbrandt ◽  
Paul A. Vaillancourt ◽  
Frédérick Chosson ◽  
Hugh Morrison
Keyword(s):  
Large Scale ◽  
Deep Convection ◽  
Weather Prediction ◽  
Kinematic Model ◽  
Horizontal Resolution ◽  
Microphysics Scheme ◽  
Global Environmental ◽  
Error Standard Deviation ◽  
Diagnostics Of State ◽  
Convection Parameterization

AbstractA parameterization for the subgrid-scale cloud and precipitation fractions has been incorporated into the Predicted Particle Properties (P3) microphysics scheme for use in atmospheric models with relatively coarse horizontal resolution. The modified scheme was tested in a simple 1D kinematic model and in the Canadian Global Environmental Multiscale (GEM) model using an operational global NWP configuration with a 25-km grid spacing. A series of 5-day forecast simulations was run using P3 and the much simpler operational Sundqvist condensation scheme as a benchmark for comparison. The effects of using P3 in a global GEM configuration, with and without the modifications, were explored through statistical metrics of common forecast fields against upper-air and surface observations. Diagnostics of state variable tendencies from various physics parameterizations were examined to identify possible sources of errors resulting from the use of the modified scheme. Sensitivity tests were performed on the coupling between the deep convection parameterization scheme and the microphysics, specifically regarding assumptions in the physical properties of detrained ice. It was found that even without recalibration of the suite of moist physical parameterizations, substituting the Sundqvist condensation scheme with the modified P3 microphysics resulted in some significant improvements to the temperature and geopotential height bias throughout the troposphere and out to day 5, but with degradation to error standard deviation toward the end of the integrations, as well as an increase in the positive bias of precipitation quantities. The modified P3 scheme was thus shown to hold promise for potential use in coarse-resolution NWP systems.

Sign in / Sign up

Export Citation Format

Share Document