Farahnaz Khosrawi

Mountain-wave-induced temperature perturbations can locally enable the formation of polar stratospheric clouds (PSCs). We examine a decade-long (2002–2012) record of ice PSCs derived from MIPAS/Envisat measurements. The points with the smallest temperature difference (ΔTice_min) between the frost point temperature (Tice) and the environmental temperature along the line of sight have been proposed and shown to provide a better estimate of the location of ice PSC observation from MIPAS. The temperature for the ice PSC observations is analyzed based on ERA5. Following this, we investigated the temperature history of the ice PSCs detected above Tice at the observation points along 24 h backward trajectories.We find that 52 % of Arctic and 26 % of Antarctic ice PSCs are detected above Tice, with pronounced clustering over mountainous terrain and in downstream regions. The backward trajectories were calculated by using the MPTRAC model, initialized at the ΔTice_min locations. Analysis of the temperature evolution along these trajectories shows that the fraction of ice PSCs at a temperature above Tice along the trajectory decreases, with the strongest decrease within the 6 h before observation. Accounting for temperature fluctuations along the air-mass histories, reduces the fractions of too warm ice PSCs at observation to 33 % in the Arctic and 9 % in the Antarctic.These results demonstrate the substantial role of orographic waves in ice PSC formation and provide observational constraints for chemistry–climate model evaluation. This contribution is based on the published analysis of Zou et al. (2024, Atmos. Chem. Phys., 24, 11759–11774, https://doi.org/10.5194/acp-24-11759-2024) .

The continuous increase in computational power comes with a corresponding demand for storage space. However, the ability to store data has hardly increased in recent years. This makes the demand for efficient storage solutions even more pressing, especially for meteorological reanalysis data. The current European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 reanalysis data already poses a major challenge for the community, but with the upcoming ERA6 reanalysis data, which will have an even higher resolution, significantly more storage space will be needed. An efficient way to store data is to use either lossy or lossless data compression methods to reduce storage requirements. To compress the meteorological data, we perform a multiresolution-analysis using multiwavelets on a hierarchy of nested grids. Since the local differences become negligibly small in regions where the data are locally smooth, we apply hard thresholding for data compression. This results in a high compression rate while preserving the accuracy of the original data. This strategy has been implemented into the Lagrangian model for Massive-Parallel Trajectory Calculation (MPTRAC) and has been successfully applied to ERA5 reanalysis data. Compression rates ranging from 1.6 to 12.6 can be achieved while at the same time maintaining the accuracy of the data within acceptable error limits. This leads to a reduction in storage of up to 93%, for example, reducing the file size of an ERA5 data file corresponding to a time instant from 4.9 GB to 389 MB. This renders the multiresolution-based grid adaptation a particularly suitable and effective approach for addressing the data storage challenges in atmospheric transport simulations.

Computer performance has increased immensely in recent years, but the ability to store data has only increased slightly. The storage requirements for the current version of the ERA5 meteorological reanalysis data provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) have increased by a factor of ∼80 compared to its predecessor ERA-Interim. This presents scientists with major challenges, especially if data covering several decades is to be stored on local computer systems. Accordingly, many compression methods have been developed in recent years with which data can be stored either lossless or lossy. Here we test three of these methods: two lossy compression methods, ZFP and Layer Packing (PCK), and the lossless compressor ZStandard (ZSTD). We investigate how the use of these compressed data affects the results of Lagrangian air parcel trajectory calculations with the Lagrangian model for Massive-Parallel Trajectory Calculations (MPTRAC). We analyzed 10-day forward trajectories that were globally distributed over the free troposphere and stratosphere. The largest transport deviations (up to 1600 km) were derived when using ZFP with the largest compression (CR=25). Using a less strong compression we could reduce the transport deviation (up to 100 km) and still obtain a significant compression (CR=7). Since ZSTD is a lossless compressor, we derive no transport deviations when using these compressed files for trajectory calculations, but do not reduce the use of disk space significantly using this compressor (reduction of ∼30 %, CR=1.5). The best compromise concerning compression efficiency and transport deviations is derived with the layer packing method PCK. The data is compressed by about 50 % (CR=2) but horizontal transport deviations do not exceed 40 km. Thus, our study shows that the PCK compression method would be valuable for application in atmospheric sciences and that with compression of the ERA5 meteorological reanalyses data one can overcome the challenges of high demand of disk space from this data set.

Computer performance has increased immensely in recent years, but the ability to store data has only increased slightly. The storage requirements for the current version of the ERA5 meteorological reanalysis data provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) have increased by a factor of ∼80 compared to its predecessor ERA-Interim. This presents scientists with major challenges, especially if data covering several decades is to be stored on local computer systems. Accordingly, many compression methods have been developed in recent years with which data can be stored either lossless or lossy. Here we test three of these methods: two lossy compression methods, ZFP and Layer Packing (PCK), and the lossless compressor ZStandard (ZSTD). We investigate how the use of these compressed data affects the results of Lagrangian air parcel trajectory calculations with the Lagrangian model for Massive-Parallel Trajectory Calculations (MPTRAC). We analyzed 10-day forward trajectories that were globally distributed over the free troposphere and stratosphere. The largest transport deviations (up to 1600 km) were derived when using ZFP with the largest compression (CR=25). Using a less strong compression we could reduce the transport deviation (up to 100 km) and still obtain a significant compression (CR=7). Since ZSTD is a lossless compressor, we derive no transport deviations when using these compressed files for trajectory calculations, but do not reduce the use of disk space significantly using this compressor (reduction of ∼30 %, CR=1.5). The best compromise concerning compression efficiency and transport deviations is derived with the layer packing method PCK. The data is compressed by about 50 % (CR=2) but horizontal transport deviations do not exceed 40 km. Thus, our study shows that the PCK compression method would be valuable for application in atmospheric sciences and that with compression of the ERA5 meteorological reanalyses data one can overcome the challenges of high demand of disk space from this data set.

Computer performance has increased immensely in recent years, but the ability to store data has only increased slightly. The storage requirements for the current version of the ERA5 meteorological reanalysis data provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) have increased by a factor of ∼80 compared to its predecessor ERA-Interim. This presents scientists with major challenges, especially if data covering several decades is to be stored on local computer systems. Accordingly, many compression methods have been developed in recent years with which data can be stored either lossless or lossy. Here we test three of these methods: two lossy compression methods, ZFP and Layer Packing (PCK), and the lossless compressor ZStandard (ZSTD). We investigate how the use of these compressed data affects the results of Lagrangian air parcel trajectory calculations with the Lagrangian model for Massive-Parallel Trajectory Calculations (MPTRAC). We analyzed 10-day forward trajectories that were globally distributed over the free troposphere and stratosphere. The largest transport deviations (up to 1600 km) were derived when using ZFP with the largest compression (CR=25). Using a less strong compression we could reduce the transport deviation (up to 100 km) and still obtain a significant compression (CR=7). Since ZSTD is a lossless compressor, we derive no transport deviations when using these compressed files for trajectory calculations, but do not reduce the use of disk space significantly using this compressor (reduction of ∼30 %, CR=1.5). The best compromise concerning compression efficiency and transport deviations is derived with the layer packing method PCK. The data is compressed by about 50 % (CR=2) but horizontal transport deviations do not exceed 40 km. Thus, our study shows that the PCK compression method would be valuable for application in atmospheric sciences and that with compression of the ERA5 meteorological reanalyses data one can overcome the challenges of high demand of disk space from this data set.

Computer performance has increased immensely in recent years, but the ability to store data has only increased slightly. The storage requirements for the current version of the ERA5 meteorological reanalysis data provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) have increased by a factor of ∼80 compared to its predecessor ERA-Interim. This presents scientists with major challenges, especially if data covering several decades is to be stored on local computer systems. Accordingly, many compression methods have been developed in recent years with which data can be stored either lossless or lossy. Here we test three of these methods: two lossy compression methods, ZFP and Layer Packing (PCK), and the lossless compressor ZStandard (ZSTD). We investigate how the use of these compressed data affects the results of Lagrangian air parcel trajectory calculations with the Lagrangian model for Massive-Parallel Trajectory Calculations (MPTRAC). We analyzed 10-day forward trajectories that were globally distributed over the free troposphere and stratosphere. The largest transport deviations (up to 1600 km) were derived when using ZFP with the largest compression (CR=25). Using a less strong compression we could reduce the transport deviation (up to 100 km) and still obtain a significant compression (CR=7). Since ZSTD is a lossless compressor, we derive no transport deviations when using these compressed files for trajectory calculations, but do not reduce the use of disk space significantly using this compressor (reduction of ∼30 %, CR=1.5). The best compromise concerning compression efficiency and transport deviations is derived with the layer packing method PCK. The data is compressed by about 50 % (CR=2) but horizontal transport deviations do not exceed 40 km. Thus, our study shows that the PCK compression method would be valuable for application in atmospheric sciences and that with compression of the ERA5 meteorological reanalyses data one can overcome the challenges of high demand of disk space from this data set.

Computer performance has increased immensely in recent years, but the ability to store data has only increased slightly. The storage requirements for the current version of the ERA5 meteorological reanalysis data provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) have increased by a factor of ∼80 compared to its predecessor ERA-Interim. This presents scientists with major challenges, especially if data covering several decades is to be stored on local computer systems. Accordingly, many compression methods have been developed in recent years with which data can be stored either lossless or lossy. Here we test three of these methods: two lossy compression methods, ZFP and Layer Packing (PCK), and the lossless compressor ZStandard (ZSTD). We investigate how the use of these compressed data affects the results of Lagrangian air parcel trajectory calculations with the Lagrangian model for Massive-Parallel Trajectory Calculations (MPTRAC). We analyzed 10-day forward trajectories that were globally distributed over the free troposphere and stratosphere. The largest transport deviations (up to 1600 km) were derived when using ZFP with the largest compression (CR=25). Using a less strong compression we could reduce the transport deviation (up to 100 km) and still obtain a significant compression (CR=7). Since ZSTD is a lossless compressor, we derive no transport deviations when using these compressed files for trajectory calculations, but do not reduce the use of disk space significantly using this compressor (reduction of ∼30 %, CR=1.5). The best compromise concerning compression efficiency and transport deviations is derived with the layer packing method PCK. The data is compressed by about 50 % (CR=2) but horizontal transport deviations do not exceed 40 km. Thus, our study shows that the PCK compression method would be valuable for application in atmospheric sciences and that with compression of the ERA5 meteorological reanalyses data one can overcome the challenges of high demand of disk space from this data set.

Computer performance has increased immensely in recent years, but the ability to store data has only increased slightly. The storage requirements for the current version of the ERA5 meteorological reanalysis data provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) have increased by a factor of ∼80 compared to its predecessor ERA-Interim. This presents scientists with major challenges, especially if data covering several decades is to be stored on local computer systems. Accordingly, many compression methods have been developed in recent years with which data can be stored either lossless or lossy. Here we test three of these methods: two lossy compression methods, ZFP and Layer Packing (PCK), and the lossless compressor ZStandard (ZSTD). We investigate how the use of these compressed data affects the results of Lagrangian air parcel trajectory calculations with the Lagrangian model for Massive-Parallel Trajectory Calculations (MPTRAC). We analyzed 10-day forward trajectories that were globally distributed over the free troposphere and stratosphere. The largest transport deviations (up to 1600 km) were derived when using ZFP with the largest compression (CR=25). Using a less strong compression we could reduce the transport deviation (up to 100 km) and still obtain a significant compression (CR=7). Since ZSTD is a lossless compressor, we derive no transport deviations when using these compressed files for trajectory calculations, but do not reduce the use of disk space significantly using this compressor (reduction of ∼30 %, CR=1.5). The best compromise concerning compression efficiency and transport deviations is derived with the layer packing method PCK. The data is compressed by about 50 % (CR=2) but horizontal transport deviations do not exceed 40 km. Thus, our study shows that the PCK compression method would be valuable for application in atmospheric sciences and that with compression of the ERA5 meteorological reanalyses data one can overcome the challenges of high demand of disk space from this data set.

Computer performance has increased immensely in recent years, but the ability to store data has only increased slightly. The storage requirements for the current version of the ERA5 meteorological reanalysis data provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) have increased by a factor of ∼80 compared to its predecessor ERA-Interim. This presents scientists with major challenges, especially if data covering several decades is to be stored on local computer systems. Accordingly, many compression methods have been developed in recent years with which data can be stored either lossless or lossy. Here we test three of these methods: two lossy compression methods, ZFP and Layer Packing (PCK), and the lossless compressor ZStandard (ZSTD). We investigate how the use of these compressed data affects the results of Lagrangian air parcel trajectory calculations with the Lagrangian model for Massive-Parallel Trajectory Calculations (MPTRAC). We analyzed 10-day forward trajectories that were globally distributed over the free troposphere and stratosphere. The largest transport deviations (up to 1600 km) were derived when using ZFP with the largest compression (CR=25). Using a less strong compression we could reduce the transport deviation (up to 100 km) and still obtain a significant compression (CR=7). Since ZSTD is a lossless compressor, we derive no transport deviations when using these compressed files for trajectory calculations, but do not reduce the use of disk space significantly using this compressor (reduction of ∼30 %, CR=1.5). The best compromise concerning compression efficiency and transport deviations is derived with the layer packing method PCK. The data is compressed by about 50 % (CR=2) but horizontal transport deviations do not exceed 40 km. Thus, our study shows that the PCK compression method would be valuable for application in atmospheric sciences and that with compression of the ERA5 meteorological reanalyses data one can overcome the challenges of high demand of disk space from this data set.