Laboratory for
Climatology and Remote Sensing

Abstract:

We present the new Precipitation REtrieval covering the TIbetan Plateau (PRETIP) as a feasibility study using the two geostationary (GEO) satellites Elektro-L2 and Insat-3D with reference to the GPM(Global PrecipitationMeasurementMission) IMERG (IntegratedMulti-satellitE Retrievals for GPM) product. The present study deals with the assignment of the rainfall rate. For precipitation rate assignment, the best-quality precipitation estimates from the gauge calibrated microwave (MW) within the IMERG product were combined with the GEO data by Random Forest (RF) regression. PRETIP was validated with independent MWprecipitation information not considered for model training and revealed a good performance on 30 min and 11 km spatio-temporal resolution with a correlation coefficient of R = 0.59 and outperforms the validation of the independentMWprecipitation with IMERG’s IR only product (R = 0.18). A comparison of PRETIP precipitation rates in 4 km resolution with daily rain gauge measurements from the Chinese Ministry of Water Resources revealed a correlation of R = 0.49. No differences in the performance of PRETIP for various elevation ranges or between the rainy (July, August) and the dry (May, September) season could be found.

Abstract:

Water availability is the most important factor for the vitality of forest ecosystems, especially in dry environments. The Mediterranean region is one of the hotspots of future climate change; therefore, data on the water cycle are urgently needed. We measured oxygen isotope compositions in creek water, precipitation, stem water, needle water, and tree-ring cellulose over one growing season to establish the relationship between isotope compositions in different compartments along a fractionation pathway. We analyzed plant material from pine trees (Pinus nigra J.F. Arn subsp. laricio (Poiret) Maire var. Corsicana Hyl. and Pinus pinaster Aiton) at five locations along an elevation gradient from sea level to 1600 m asl. We traced back the oxygen isotope composition from source to sink in tree-ring cellulose in order to identify the water sources used by the trees, and to quantify the extent of isotope fractionation processes. Our results showed that the trees used different water sources over the course of the growing season, ranging from winter snow meltwater to summer precipitation at higher sites and deep soil water reservoirs at coastal sites. Needle water enrichment was higher at higher elevation sites than at coastal locations, highlighting the importance of site-specific climate conditions on the isotopic composition values in tree material. Water availability seems to be most restricted at the highest site, making these trees most vulnerable to climate change.

Abstract:

The island of Corsica in the western Mediterranean is characterized by a pronounced topography in which local breeze systems develop in the diurnal cycle. In interaction with the large-scale synoptic situation, various precipitation events occur, which are classified in this study with regard to their duration and intensity. For this purpose, the island was grouped into five precipitation regimes using a cluster analysis, namely the western coastal area, the central mountainous region, the southern coastal area, the northeast coastal area, and the eastern coastal area. Based on principal component analysis using mean sea level pressure (mslp) obtained from ERA5 reanalysis (the fifth generation of the European Centre for Medium-Range Weather Forecasts, ECMWF), six spatial patterns were identified which explain 98% of the large-scale synoptic situation, while the diurnal breeze systems within the regimes characterize local drivers. It is shown that on radiation days with weak large-scale pressure gradients, pronounced local circulations in mountainous regions are coupled with sea breezes, leading to a higher number of short and intense precipitation events. Meridional circulation patterns lead to more intensive precipitation events on the eastern part of the island (30% intensive events with meridional patterns on the east side compared to 11% on the west side). On the west side of Corsica, however, coastal precipitation events are seldom and less intense than further inland, which can be attributed to the influence of the topography in frontal passages.

Abstract:

Abstract Conversion of tropical forests is among the primary causes of global environmental change. The loss of their important environmental services has prompted calls to integrate ecosystem services (ES) in addition to socio-economic objectives in decision-making. To test the effect of accounting for both ES and socio-economic objectives in land-use decisions, we develop a new dynamic approach to model deforestation scenarios for tropical mountain forests. We integrate multi-objective optimization of land allocation with an innovative approach to consider uncertainty spaces for each objective. These uncertainty spaces account for potential variability among decision-makers, who may have different expectations about the future. When optimizing only socio-economic objectives, the model continues the past trend in deforestation (1975–2015) in the projected land-use allocation (2015–2070). Based on indicators for biomass production, carbon storage, climate and water regulation, and soil quality, we show that considering multiple ES in addition to the socio-economic objectives has heterogeneous effects on land-use allocation. It saves some natural forest if the natural forest share is below 38%, and can stop deforestation once the natural forest share drops below 10%. For landscapes with high shares of forest (38%–80% in our study), accounting for multiple ES under high uncertainty of their indicators may, however, accelerate deforestation. For such multifunctional landscapes, two main effects prevail: (a) accelerated expansion of diversified non-natural areas to elevate the levels of the indicators and (b) increased landscape diversification to maintain multiple ES, reducing the proportion of natural forest. Only when accounting for vascular plant species richness as an explicit objective in the optimization, deforestation was consistently reduced. Aiming for multifunctional landscapes may therefore conflict with the aim of reducing deforestation, which we can quantify here for the first time. Our findings are relevant for identifying types of landscapes where this conflict may arise and to better align respective policies.

Abstract:

Abstract The Atacama Desert is the driest non-polar desert on Earth, presenting precarious conditions for biological activity. In the arid coastal belt, life is restricted to areas with fog events that cause almost daily wet–dry cycles. In such an area, we discovered a hitherto unknown and unique ground covering biocenosis dominated by lichens, fungi, and algae attached to grit-sized (~6 mm) quartz and granitoid stones. Comparable biocenosis forming a kind of a layer on top of soil and rock surfaces in general is summarized as cryptogamic ground covers (CGC) in literature. In contrast to known CGC from arid environments to which frequent cyclic wetting events are lethal, in the Atacama Desert every fog event is answered by photosynthetic activity of the soil community and thus considered as the desert's breath. Photosynthesis of the new CGC type is activated by the lowest amount of water known for such a community worldwide thus enabling the unique biocenosis to fulfill a variety of ecosystem services. In a considerable portion of the coastal Atacama Desert, it protects the soil from sporadically occurring splash erosion and contributes to the accumulation of soil carbon and nitrogen as well as soil formation through bio-weathering. The structure and function of the new CGC type are discussed, and we suggest the name grit–crust. We conclude that this type of CGC can be expected in all non-polar fog deserts of the world and may resemble the cryptogam communities that shaped ancient Earth. It may thus represent a relevant player in current and ancient biogeochemical cycling.

Abstract:

Coherent wind doppler lidar (CWDL) is a cost-effective way to estimate wind power potential at hub height without the need to build a meteorological tower. However, fog and low stratus (FLS) can have a negative impact on the availability of lidar measurements. Information about such reductions in wind data availability for a prospective lidar deployment site in advance is beneficial in the planning process for a measurement strategy. In this paper, we show that availability reductions by FLS can be estimated by comparing time series of lidar measurements, conducted with WindCubes v1 and v2, with time series of cloud base altitude (CBA) derived from satellite data. This enables us to compute average maps (2006–2017) of estimated availability, including FLS-induced data losses for Germany which can be used for planning purposes. These maps show that the lower mountain ranges and the Alpine regions in Germany often reach the critical data availability threshold of 80% or below. Especially during the winter time special care must be taken when using lidar in southern and central regions of Germany. If only shorter lidar campaigns are planned (3–6 months) the representativeness of weather types should be considered as well, because in individual years and under persistent weather types, lowland areas might also be temporally affected by higher rates of data losses. This is shown by different examples, e.g., during radiation fog under anticyclonic weather types.

Abstract:

Data on the distribution of tree species are often requested by forest managers, inventory agencies, foresters as well as private and municipal forest owners. However, the automated detection of tree species based on passive remote sensing data from aerial surveys is still not sufficiently developed to achieve reliable results independent of the phenological stage, time of day, season, tree vitality and prevailing atmospheric conditions. Here, we introduce a novel tree species classification approach based on high resolution RGB image data gathered during automated UAV flights that overcomes these insufficiencies. For the classification task, a computationally lightweight convolutional neural network (CNN) was designed. We show that with the chosen CNN model architecture, average classification accuracies of 92% can be reached independently of the illumination conditions and the phenological stages of four different tree species. We also show that a minimal ground sampling density of 1.6 cm/px is needed for the classification model to be able to make use of the spatial-structural information in the data. Finally, to demonstrate the applicability of the presented approach to derive spatially explicit tree species information, a gridded product is generated that yields an average classification accuracy of 88%.

Abstract:

The Atacama Desert is considered as one of the driest places on Earth. At the coastline, however, small-scale fog oases harbor a specialized vegetation and fauna, living from moisture by fog, which is used by humans to feed water demands of industrial projects. To date, knowledge about fog and low stratus (FLS) clouds as well as their physical properties is limited in that only local observations or spatial products from satellites with coarse resolutions are available generally failing to capture local patterns resulting from the complex topography. Consequently, we provide the first climatology of FLS with 30m spatial resolution based on over 400 Landsat scenes acquired since 1986. The new product provides valuable estimates of FLS optical and micro-physical properties. FLS over the Pacific Ocean featured cloud optical depth values around 13.5 declining over land to 4.2. Effective radii were around 5.3 μm. Liquid water path was between 71.0 − gm 2 over the Ocean and 14.9 − gm 2 over land surfaces. The climatologies of the new Landsat product were successfully validated against those of the MODIS cloud property product over homogeneous surfaces. Over areas with heterogeneous topographies, the new product outperforms existing ones with coarse spatial resolutions if compared against in situ measurements. This shows the general need for cloud products with high spatial resolutions in areas where the development of small scale clouds is favored e.g., by a complex topography leading to systematical biases in existing retrievals.

Abstract:

Interest in atmosphere-surface flux modeling over the mountainous regions of the globe has increased recently, with a major focus on the prediction of water, carbon and other functional indicators in natural and disturbed conditions. However, less research has been centered on exploring energy fluxes (net radiation; sensible, latent and soil heat) and actual evapotranspiration (ETa) over the Neotropical Andean biome of the páramo. The present study assesses the implementation and parameterization of a state-of-art Land-Surface Model (LSM) for simulation of these fluxes over two representative páramo catchments of southern Ecuador. We evaluated the outputs of the LSM Community Land Model (CLM ver. 4.0) with (i) ground-level flux observations from the first (and highest) Eddy Covariance (EC) tower of the Northern Andean páramos; (ii) spatial ETa estimates from the energy balance-based model METRIC (based on Landsat imagery); and (iii) derived ETa from the closure of the water balance (WB). CLM’s energy predictions revealed a significant underestimation on net radiation, which impacts the sensible and soil heat fluxes (underestimation), and delivers a slight overestimation on latent heat flux. Modeled CLM ETa showed acceptable goodness-of-fit (Pearson R = 0.82) comparable to ETa from METRIC (R = 0.83). Contrarily, a poor performance of ETa WB was observed (R = 0.46). These findings provide solid evidence on the CLM’s accuracy for the ETa modeling, and give insights in the selection of other ETa methods. The study contributes to a better understanding of ecosystem functioning in terms of water loss through evaporative processes, and might help in the development of future LSMs’ implementations focused on climate / land use change scenarios for the páramo.

Abstract:

In remote areas with steep topography, such as the Tropical Andes, reliable precipitation data with a high temporal resolution are scarce. Therefore, studies focusing on the diurnal properties of precipitation are hampered. In this paper, we investigated two years of data from Micro-Rain Radars (MRR) in Cuenca, Ecuador, and Huaraz, Peru, from February 2017 to January 2019. This data allowed for a detailed study on the temporal precipitation characteristics, such as event occurrences and durations at these two locations. Our results showed that the majority of precipitation events had durations of less than 3 h. In Huaraz, precipitation has a distinct annual and diurnal cycle where precipitation in the rainy season occurred predominantly in the afternoon. These annual and diurnal cycles were less pronounced at the site in Cuenca, especially due to increased nocturnal precipitation events compared to Huaraz. Furthermore, we used a fuzzy logic classification of fall velocities and rainfall intensities to distinguish different precipitation types. This classification showed that nightly precipitation at both locations was predominantly stratiform, whereas (thermally induced) convection occurred almost exclusively during the daytime hours

Abstract:

Due to the integration of fluctuating weather-dependent energy sources into the grid the importance of weather and power forecasts grows constantly. This paper describes the implementation of a short-term forecast of solar surface irradiance named SESORA (seamless sola radiation). It is based on the the optical flow of effective cloud albedo and available for Germany and parts of Europe. After the clouds are shifted by applying cloud motion vectors, solar radiation i calculated with SPECMAGIC NOW(Spectrally Resolved Mesoscale Atmospheric Global Irradianc Code), which computes the global irradiation spectrally resolved from satellite imagery. Due to the high spatial and temporal resolution of satellite measurements, solar radiation can be forecasted from 15 min up to 4 h or more with a spatial resolution of 0.05. An extensive validation of thi short-term forecast is presented in this study containing two different validations based on eithe area or stations. The results are very promising as the mean RMSE (Root Mean Square Error) of thi study equals 59W/m2 (absolute bias = 42W/m2) after 15 min, reaches its maximum of 142W/m (absolute bias = 97W/m2) after 165 min, and slowly decreases after that due to the setting of the sun After a brief description of the method itself and the method of the validation the results will be presented and discussed.

Abstract:

Fog is a weather phenomenon that affects all of us, both in the business and private sectors. On the one hand it is vital for entire ecosystems worldwide, on the other hand it can lead to immense economic damage (airport etc.) and even life-threatening situations, for example in road traffic. The positive as well as the negative implications demonstrate the urgency of a good understanding of fog events and their prediction. Fog forecasting has been a subject of research for many years. However, due to the complex interactions of a wide variety of variables relevant for fog formation, accurate prediction still remains very difficult. One reason for this is that the simulation of parameter interactions which are difficult to capture by modelling is affected by errors due to the complexity of the system. Another reason is the neglected but most likely important combination of temporal and spatial variables, which is currently not taken into account by the models. This is mainly due to the lack of suitable approaches for the combination of temporally high-resolution station measurements of fog-relevant variables and area-wide high-resolution satellite data.

Abstract:

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.