A novel day/night-technique for area-wide precipitation retrieval over Central Europe using MSG SEVIRI data

Knowledge of the spatio-temporal precipitation distribution is of great value in agriculture, water engineering, climatology and risk management. So far, no adequate method existed for the detection and monitoring of precipitation at high temporal and spatial resolutions in most parts of the world where radar networks are not available. Due to spectral constraints, existing retrieval techniques rely on a relationship between rainfall probability and intensity and the cloud top temperature measured in an infrared channel. These techniques show considerable drawbacks concerning precipitation processes in the mid-latitudes. Improved techniques for rain area identification based on spectral enhancements of new generation satellite systems used to be only available on polar orbiting platforms with poor temporal resolutions. Furthermore, these algorithms are only applicable during day-time. With the advent of Meteosat Second Generation (MSG) Spinning-Enhanced Visible and InfraRed Imager (SEVIRI) in 2004, a geostationary satellite system with significantly improved spectral and spatial resolutions has become available. The central aim of the present study therefore was to develop a novel method for operational precipitation detection during day- and night-time based on MSG SEVIRI data. The focus of the newly developed scheme lies on precipitation processes in the mid-latitudes in connection with extra-tropical cyclones. It is therefore not only applicable to convectively dominated rain areas but also to precipitating cloud areas of advective-stratiform character. The newly developed rainfall retrieval scheme based on the advanced second-generation GEO system MSG SEVIRI rests upon the following conceptual model: • Precipitating cloud areas are characterized by a sufficiently high cloud water path and ice particles in the upper part. • Cloud areas with higher rainfall intensities are characterized by a higher cloud water path and a higher amount of ice particles in the upper part. • Convective clouds with very high rainfall intensities are characterized by a large vertical extension and a high rising cold cloud top. Based on this conceptual design, the new retrieval scheme consists of an entirely new methodology compiling novel and innovative algorithms and approaches. The following three components are the focal parts of the novel technique: • A new algorithm for the identification of the rain area during day- and night-time was developed for SEVIRI. The method allows not only a proper detection of mainly convective rain areas but also enables the detection of advective-stratiform precipitation (e.g. in connection with mid-latitude frontal systems). It is based on information about the CWP and the cloud phase in the upper cloud regions. • An infrared retrieval technique appropriate for convective precipitation processes in the mid-latitudes was successfully transferred and adapted to MSG SEVIRI. The phenomenon of positive brightness temperature differences between the WV and IR channels (dTWV-IR), which enables the detection and classification of convectively dominated raining cloud areas was investigated for the WV and IR channels of SEVIRI. Based on radiative transfer calculations, which revealed the existence of positive ΔTWV-IR for all SEVIRI WV-IR differences, the dTWV technique could be applied and transferred to SEVIRI. • A new technique for precipitation process and rainfall intensity separation was developed for SEVIRI. The process separation and the further subdivision relies on information about the cloud top height, the cloud water path and the cloud phase in the upper parts. The subdivision is realized in a stepwise manner. In a first step the rain area is separated into the subareas of convective and advective-stratiform precipitation processes. In the following both separated process areas are divided into subareas of differing rainfall intensities. The process separation and the subdivision of the convective precipitation area relies on information about the cloud top height. The subdivision of the advective-stratiform precipitation area is based on information about the CWP and the particle phase in the upper parts of the cloud. The rain area and the process-oriented rainfall intensities detected and classified by the newly developed retrieval technique were validated against corresponding ground-based radar data of Germany, representative for mid-latitude precipitation processes. The results of the validation study indicate persuading performance of the new algorithm concerning rain area identification as well as process and intensity differentiation and indicate the stability of the introduced conceptual design

Land Cover Change in the Andes of Southern Ecuador — Patterns and Drivers

In the megadiverse tropical mountain forest in the Andes of southern Ecuador, a global biodiversity hotspot, the use of fire to clear land for cattle ranching is leading to the invasion of an aggressive weed, the bracken fern, which is threatening diversity and the provisioning of ecosystem services. To find sustainable land use options adapted to the local situation, a profound knowledge of the long-term spatiotemporal patterns of land cover change and its drivers is necessary, but hitherto lacking. The complex topography and the high cloud frequency make the use of remote sensing in this area a challenge. To deal with these conditions, we pursued specific pre-processing steps before classifying five Landsat scenes from 1975 to 2001. Then, we quantified land cover changes and habitat fragmentation, and we investigated landscape changes in relation to key spatial elements (altitude, slope, and distance from roads). Good classification results were obtained with overall accuracies ranging from 94.5% to 98.5% and Kappa statistics between 0.75 and 0.98. Forest was strongly fragmented due to the rapid expansion of the arable frontier and the even more rapid invasion by bracken. Unexpectedly, more bracken-infested areas were converted to pastures than vice versa, a practice that could alleviate pressure on forests if promoted. Road proximity was the most important spatial element determining forest loss, while for bracken the altitudinal range conditioned the degree of invasion in deforested areas. The annual deforestation rate changed notably between periods: ~1.5% from 1975 to 1987, ~0.8% from 1987 to 2000, and finally a very high rate of ~7.5% between 2000 and 2001. We explained these inconstant rates through some specific interrelated local and national political and socioeconomic drivers, namely land use policies, credit and tenure incentives, demography, and in particular, a severe national economic and bank crisis

Intercomparison of Gridded Precipitation Datasets over a Sub-Region of the Central Himalaya and the Southwestern Tibetan Plateau

Precipitation is a central quantity of hydrometeorological research and applications. Especially in complex terrain, such as in High Mountain Asia (HMA), surface precipitation observations are scarce. Gridded precipitation products are one way to overcome the limitations of ground truth observations. They can provide datasets continuous in both space and time. However, there are many products available, which use various methods for data generation and lead to different precipitation values. In our study we compare nine different gridded precipitation products from different origins (ERA5, ERA5-Land, ERA-interim, HAR v2 10 km, HAR v2 2 km, JRA-55, MERRA-2, GPCC and PRETIP) over a subregion of the Central Himalaya and the Southwest Tibetan Plateau, from May to September 2017. Total spatially averaged precipitation over the study period ranged from 411 mm (GPCC) to 781 mm (ERA-Interim) with a mean value of 623 mm and a standard deviation of 132 mm. We found that the gridded products and the few observations, with few exceptions, are consistent among each other regarding precipitation variability and rough amount within the study area. It became obvious that higher grid resolution can resolve extreme precipitation much better, leading to overall lower mean precipitation spatially, but higher extreme precipitation events. We also found that generally high terrain complexity leads to larger differences in the amount of precipitation between products. Due to the considerable differences between products in space and time, we suggest carefully selecting the product used as input for any research application based on the type of application and specific research question. While coarse products such as ERA-Interim or ERA5 that cover long periods but have coarse grid resolution have previously shown to be able to capture long-term trends and help with identifying climate change features, this study suggests that more regional applications, such as glacier mass-balance modeling, require higher spatial resolution, as is reproduced, for example, in HAR v2 10 km.Peer Reviewe

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

The Influence of Drop Size Distributions on the Relationship between Liquid Water Content and Radar Reflectivity in Radiation Fogs

This study investigates the temporal dynamics of the drop size distribution (DSD) and its influence on the relationship between the liquid water content (LWC) and the radar reflectivity (Z) in fogs. Data measured during three radiation fog events at the Marburg Ground Truth and Profiling Station in Linden-Leihgestern, Germany, form the basis of this analysis. Specifically, we investigated the following questions: (1) Do the different fog life cycle stages exhibit significantly different DSDs? (2) Is it possible to identify characteristic DSDs for each life cycle stage? (3) Is it possible to derive reliable Z-LWC relationships by means of a characteristic DSD? The results showed that there were stage-dependent differences in the fog life cycles, although each fog event was marked by unique characteristics, and a general conclusion about the DSD during the different stages could not be made. A large degree of variation within each stage also precludes the establishment of a representative average spectrum

A novel day/night-technique for area-wide precipitation retrieval over Central Europe using MSG SEVIRI data

Knowledge of the spatio-temporal precipitation distribution is of great value in agriculture, water engineering, climatology and risk management. So far, no adequate method existed for the detection and monitoring of precipitation at high temporal and spatial resolutions in most parts of the world where radar networks are not available. Due to spectral constraints, existing retrieval techniques rely on a relationship between rainfall probability and intensity and the cloud top temperature measured in an infrared channel. These techniques show considerable drawbacks concerning precipitation processes in the mid-latitudes. Improved techniques for rain area identification based on spectral enhancements of new generation satellite systems used to be only available on polar orbiting platforms with poor temporal resolutions. Furthermore, these algorithms are only applicable during day-time. With the advent of Meteosat Second Generation (MSG) Spinning-Enhanced Visible and InfraRed Imager (SEVIRI) in 2004, a geostationary satellite system with significantly improved spectral and spatial resolutions has become available. The central aim of the present study therefore was to develop a novel method for operational precipitation detection during day- and night-time based on MSG SEVIRI data. The focus of the newly developed scheme lies on precipitation processes in the mid-latitudes in connection with extra-tropical cyclones. It is therefore not only applicable to convectively dominated rain areas but also to precipitating cloud areas of advective-stratiform character. The newly developed rainfall retrieval scheme based on the advanced second-generation GEO system MSG SEVIRI rests upon the following conceptual model: • Precipitating cloud areas are characterized by a sufficiently high cloud water path and ice particles in the upper part. • Cloud areas with higher rainfall intensities are characterized by a higher cloud water path and a higher amount of ice particles in the upper part. • Convective clouds with very high rainfall intensities are characterized by a large vertical extension and a high rising cold cloud top. Based on this conceptual design, the new retrieval scheme consists of an entirely new methodology compiling novel and innovative algorithms and approaches. The following three components are the focal parts of the novel technique: • A new algorithm for the identification of the rain area during day- and night-time was developed for SEVIRI. The method allows not only a proper detection of mainly convective rain areas but also enables the detection of advective-stratiform precipitation (e.g. in connection with mid-latitude frontal systems). It is based on information about the CWP and the cloud phase in the upper cloud regions. • An infrared retrieval technique appropriate for convective precipitation processes in the mid-latitudes was successfully transferred and adapted to MSG SEVIRI. The phenomenon of positive brightness temperature differences between the WV and IR channels (dTWV-IR), which enables the detection and classification of convectively dominated raining cloud areas was investigated for the WV and IR channels of SEVIRI. Based on radiative transfer calculations, which revealed the existence of positive ΔTWV-IR for all SEVIRI WV-IR differences, the dTWV technique could be applied and transferred to SEVIRI. • A new technique for precipitation process and rainfall intensity separation was developed for SEVIRI. The process separation and the further subdivision relies on information about the cloud top height, the cloud water path and the cloud phase in the upper parts. The subdivision is realized in a stepwise manner. In a first step the rain area is separated into the subareas of convective and advective-stratiform precipitation processes. In the following both separated process areas are divided into subareas of differing rainfall intensities. The process separation and the subdivision of the convective precipitation area relies on information about the cloud top height. The subdivision of the advective-stratiform precipitation area is based on information about the CWP and the particle phase in the upper parts of the cloud. The rain area and the process-oriented rainfall intensities detected and classified by the newly developed retrieval technique were validated against corresponding ground-based radar data of Germany, representative for mid-latitude precipitation processes. The results of the validation study indicate persuading performance of the new algorithm concerning rain area identification as well as process and intensity differentiation and indicate the stability of the introduced conceptual design

A Hybrid Approach for Fog Retrieval Based on a Combination of Satellite and Ground Truth Data

Fog has a substantial influence on various ecosystems and it impacts economy, traffic systems and human life in many ways. In order to be able to deal with the large number of influence factors, a spatially explicit high-resoluted data set of fog frequency distribution is needed. In this study, a hybrid approach for fog retrieval based on Meteosat Second Generation (MSG) data and ground truth data is presented. The method is based on a random forest (RF) machine learning model that is trained with cloud base altitude (CBA) observations from Meteorological Aviation Routine Weather Reports (METAR) as well as synoptic weather observations (SYNOP). Fog is assumed where the model predicts CBA values below a dynamically derived threshold above the terrain elevation. Cross validation results show good accordance with observation data with a mean absolute error of 298 m in CBA values and an average Heidke Skill Score of 0.58 for fog occurrence. Using this technique, a 10 year fog baseline climatology with a temporal resolution of 15 min was derived for Europe for the period from 2006 to 2015. Spatial and temporal variations in fog frequency are analyzed. Highest average fog occurrences are observed in mountainous regions with maxima in spring and summer. Plains and lowlands show less overall fog occurrence but strong positive anomalies in autumn and winter

Estimating High Spatio-Temporal Resolution Rainfall from MSG1 and GPM IMERG Based on Machine Learning: Case Study of Iran

A new satellite-based technique for rainfall retrieval in high spatio-temporal resolution (3 km, 15 min) for Iran is presented. The algorithm is based on the infrared bands of the Meteosat Second Generation Spinning Enhanced Visible and Infrared Imager (MSG SEVIRI). Random forest models using microwave-only rainfall information of the Integrated Multi-SatEllite Retrieval for the Global Precipitation Measurement (GPM) (IMERG) product as a reference were developed to (i) delineate the rainfall area and (ii) to assign the rainfall rate. The method was validated against independent microwave-only GPM IMERG rainfall data not used for model training. Additionally, the new technique was validated against completely independent gauge station data. The validation results show a promising performance of the new rainfall retrieval technique, especially when compared to the GPM IMERG IR-only rainfall product. The standard verification scored an average Heidke Skill Score of 0.4 for rain area delineation and an average R between 0.1 and 0.7 for rainfall rate assignment, indicating uncertainties for the Lut Desert area and regions with high altitude gradients

Harmonization of Meteosat First and Second Generation Datasets for Fog and Low Stratus Studies

Operational weather satellites, dating back to 1970s, currently provide the best basis for climatological investigations, such as an analysis of changes in the cloud cover. Because clouds are highly dynamic in time, temporally high-resolution data from the geostationary orbit are preferred in order to take variations in the diurnal cycles into account. For such studies, a consistent dataset in space and time is mandatory, but not yet available. Ground-based point measurements of various cloud parameters, such as ceiling, visibility, and cloud type are often sparsely spread and inconsistent, making it difficult to derive reliable spatio-temporal information over large areas. The Meteosat program has generally provided suitable data from over Europe since 1977, but different spatial, spectral, and radiometric resolution of the instruments of the individual satellites, including early-years calibration uncertainties, makes harmonization necessary to finally derive a time series applicable to any kind of climatological study. In this study, a machine learning-based approach has been employed to generate a long-term consistent dataset with high spatio-temporal resolution and extensive coverage over Europe by the harmonization of Meteosat First Generation (MFG) and Meteosat Second Generation (MSG) satellite datasets (1991–2020). A random forest (RF) regressor is trained on the overlap period (2004–2006), where datasets of both satellite generation (MFG and MSG) are available to predict MFG Water Vapour (WV) and Infrared (IR) channels brightness temperature (BT) values based on MSG channels. The aim of the study is to synthesize MFG MVIRI data from MSG SEVIRI to generate a consistent MFG time series. The results indicate a good match of MFG synthesized data with the original MFG data with a mean absolute error of 0.7 K for the WV model and 1.6 K for the IR model, and an out-of-bag (OOB) R² score of 0.98 for both the models. Based on the trained models, the MFG scenes are synthesized from the MSG scenes for the years from 2006 to 2020. The long-term homogeneity of the generated time series is analyzed. The harmonized dataset will be applied to generate a continuous time series on fog and low stratus (FLS) occurrence for a climatological time scale of 30 years

Spatiotemporal High-Resolution Cloud Mapping with a Ground-Based IR Scanner

The high spatiotemporal variability of clouds requires automated monitoring systems. This study presents a retrieval algorithm that evaluates observations of a hemispherically scanning thermal infrared radiometer, the NubiScope, to produce georeferenced, spatially explicit cloud maps. The algorithm uses atmospheric temperature and moisture profiles and an atmospheric radiative transfer code to differentiate between cloudy and cloudless measurements. In case of a cloud, it estimates its position by using the temperature profile and viewing geometry. The proposed algorithm was tested with 25 cloud maps generated by the Fmask algorithm from Landsat 7 images. The overall cloud detection rate was ranging from 0.607 for zenith angles of 0 to 10° to 0.298 for 50–60° on a pixel basis. The overall detection of cloudless pixels was 0.987 for zenith angles of 30–40° and much more stable over the whole range of zenith angles compared to cloud detection. This proves the algorithm’s capability in detecting clouds, but even better cloudless areas. Cloud-base height was best estimated up to a height of 4000 m compared to ceilometer base heights but showed large deviation above that level. This study shows the potential of the NubiScope system to produce high spatial and temporal resolution cloud maps. Future development is needed for a more accurate determination of cloud height with thermal infrared measurements