To understand the influence of the Sun on Earth's system, long and accurate measurements of solar irradiance are a prerequisite. The available direct measurements of solar irradiance since 1978 are clearly not sufficient for this purpose. This stimulated development of models used to reconstruct past solar irradiance variations from alternative observations. The main driver of the irradiance variations on time scales of days to millennia is the evolution of the solar surface magnetic field in form of dark sunspots and bright faculae and network. Therefore, models require input data describing the contributions of these various magnetic regions on the Sun at earlier times. Unfortunately, records that can be used to describe the facular and network contributions are barely longer than the direct irradiance measurements. Thus, irradiance reconstructions to earlier periods have to rely on sunspot data alone. Data that have hardly been used for solar irradiance reconstructions until now are full-disc solar observations in the Ca II K line. Such data exist since 1892 from various observatories and include all the needed information describing faculae and the network. However, they are plagued by a bunch of various problems and artefacts, and recovering the non-linear response of the photographic material to the radiation is non-trivial since the required information is usually missing, too. We have developed a method to process Ca II K observations from various sources and demonstrated the higher accuracy achieved by our method compared to other techniques presented in the literature. Here we use the carefully reduced Ca II K observations from multiple archives to reconstruct solar irradiance variations.

The Permian Basin has become the United States’ largest producer of oil over the past decade. Along with the rise in production, there has been an increase in the rate of low magnitude earthquakes, some of which have been associated with hydrocarbon extraction and wastewater injection. A detailed knowledge of changes to the subsurface can aid in understanding the causes of seismicity, and these changes can be inferred from InSAR surface deformation measurements. In this study, we show that both cm-level cumulative deformation, as well as mm-level coseismic deformation signals, are detectable in West Texas. In a region west of Mentone, TX, we reconstructed the subtle coseismic deformation signal on the order of ~5 mm associated with the recent M4.9 earthquake. Over ~100,000 km2 of the Permian Basin, we created annual cumulative LOS deformation maps, decomposing into vertical and eastward components where overlapping data are available. These maps contain numerous subsidence and uplift features near active production and disposal wells. The most important deformation signatures are linear streaks that extend tens of kilometers near Pecos, TX, where a cluster of increased seismic events was cataloged by TexNet. As validated by independent GPS data, our InSAR processing strategy achieved millimeter-level accuracy. A careful treatment of the InSAR tropospheric noise, which can be as large as 15 cm in West Texas, is required to detect surface deformation signals with such low signal-to-noise ratio. We developed an outlier removal technique based on robust statistics to detect the presence of strong, non-Gaussian noise. We compared the surface deformation solutions of multiple InSAR time series methods, and all of them produced more accurate and consistent deformation trends after removing outlier InSAR measurements. We are exploring a Bayesian generalization of SBAS velocity estimation by including probabilistic data rejection to determine which pixels should be excluded from the model fitting. This technique provides a full posterior distribution of the model parameters along with the best-fit surface velocity.

Lunar mare basalts are the products of their corresponding parent magma compositions, sourced from the lunar upper mantle. The lunar mantle has been repeatedly modeled through numeric simulations to reflect lunar magma ocean (LMO) crystallization, resulting in an early-stage anorthositic crust and immediately underlying, late-stage, KREEP-rich and ilmenite-rich layer. This negatively buoyant layer is expected to have induced mixing with the underlying mantle, potentially to the core-mantle boundary. The lunar mare basalts, in this context, reflect mantle sources that are variably mixed between pristine mantle compositions and the dense ilmenite-rich layer. In order to constrain the geometry of lunar mantle heterogeneity, we simultaneously examined multiple mare basalt characteristics to extract significant multivariate patterns that might lend insight into the nature of this mixing-induced heterogeneity. Using two fundamental machine learning approaches and a newly compiled database of Apollo basalt characteristics (ApolloBasaltDB), we conducted a preliminary investigation, holding the assumptions that 1) mare basalts are assumed to retain the majority of their original characteristics at the time of extrusion: texture, isotopic age, major element composition, mineral mode, and geographic occurrence; 2) negligible basalt alteration occurred due to the lack of an atmosphere; and 3) impact gardening did not have significant bearing on final geographic location of basalt samples based on our nearside spatial partitions. The results of cluster and principal component analyses over changing spatial basalt groupings suggest that lunar nearside changes in major element concentrations and mineral modes vary spatially. Al2O3 concentrations increase in diversity within the Procellarum KREEP Terrane (PKT) compared to older regions immediately exterior to the eastern PKT, while a general nearside trend appears to suggest that ilmenite (TiO2) diversity comes at the expense of plagioclase (Al2O3) diversity. Cluster analysis suggests PKT perimeter rifting may have tapped into increasingly Ti-rich sources as rifting proceeded SE to NW. By establishing such trends over varying spatial scales through multivariate processing, the changing strengths of these surface correlative patterns may indicate the changing conditions (including temporal) of the immediately underlying mantle at the time of extrusion.

Data maturity describes the degree of the formalisation/standardisation of a data object with respect to FAIRness and quality of the (meta-) data. Therefore, a high (meta-) data maturity increases the reusability of data. Moreover, it is an important topic in data management, which is reflected by a growing number of tools and theories trying to measure it, e.g. the FAIR testing tools assessed by RDA(1) or the NOAA maturity matrix(2). If the results of stewardship tasks cannot be shown directly in the metadata, reusers of data cannot easily recognise which data is easy to reuse. For example, the DataCite Metadata Schema does not provide an explicit property to link/store information on data maturity (e.g. FAIRness or quality of data/metadata). The AtMoDat project (3, Atmospheric Model Data) aims to improve the reusability of published atmospheric model data by scientists, the public sector, companies, and other stakeholders. These data are valuable because they form the basis to understand and predict natural events, including the atmospheric circulation and ultimately the atmospheric and planetary energy budget. As most atmospheric data has been published with DataCite DOIs, it is of high importance that the maturity of the datasets can be easily found in the DOI’s Metadata. Published data from other fields of research would also benefit from easily findable maturity information. Therefore, we developed a Maturity Indicator concept and propose to introduce it as a new property in the DataCite Metadata Schema. This indicator is generic and independent of any scientific discipline and data stewardship tool. Hence, it can be used in a variety of research fields. 1 https://doi.org/10.15497/RDA00034 2 Peng et al., 2015: https://doi.org/10.2481/dsj.14-049 3 www.atmodat.de

Many aquatic environments are dominated by muddy sediments. These cohesive sediments, however, often contain a mixture of sand, mud and organic material, giving rise to complex interactional behaviour, the nature of which is often controlled by bio-physical attributes. An understanding of these complex interactions is paramount in the accurate prediction of sediment transport processes in numerical models, facilitating monitoring and management of marine environments. Calibration of such models relies on quantitative erodibility and depositional data. Muddy sediments flocculate; a process impacted by complex sedimentary and hydrodynamic interactions. The degree of sediment stability describes the degree of flocculation and depends on interactive forces (including bonding cohesion) between suspended particulate matter and turbulent shear stress, as well as mineralogy and biological composition. Erodibility and deposition properties rely greatly on the formation and break-up of these flocs, in turn impacting processes of sediment transport. This study examines, through the use and comparison of various data sets, aspects of both erodibility and deposition for several different sedimentary conditions. Collation of a range of quantitative field and laboratory-derived sedimentary and hydrodynamical data sets (e.g. sediment composition, floc properties, bed density, mass erosion rates, erosion thresholds, suspended particular matter concentration, turbulent shear stress) from a range of aquatic scenarios (including estuaries, intertidal areas, shelf seas, and lakes) are utilised to investigate the impacts of related controlling and influencing parameters on sediment transport, in particular to assess coastal erosion and sustainability. Case studies include: water quality monitoring, contaminated sediments, and dredging applications; these will be used to demonstrate / illustrate various applications of this sedimentary-hydrodynamic investigation. This research augments our understanding of the interactive processes within different cohesive sediments, providing quantitative analysis to inform and ultimately improve our mathematical representation of bio-physical sedimentary processes for implementation within predictive numerical modelling.

The Space Physics Data Facility (SPDF https://spdf.gsfc.nasa.gov), as the non-solar NASA Heliophysics active final archive, works with current operating missions and the heliophysics community to ingest, preserve and serve a wide range of past and current public science-quality data from the mesosphere into the furthest reach of deep-space exploration. SPDF facilitates scientific analysis of multi-instrument and multi-mission datasets to enhance the science return of the many missions. SPDF develops and maintains the Common Data Format (CDF) and the associated ISTP/SPDF metadata guidelines. SPDF services include CDAWeb, which supports both survey and burst mode data with graphics, listings and data superset/subset functions. All public data held by SPDF are also available for direct file download by HTTPS or FTPS links from the SPDF home page (https://spdf.gsfc.nasa.gov). SPDF is currently receiving and serving from missions including Parker Solar Probe, MMS, Van Allen Probes, THEMIS/ARTEMIS, GOLD, ACE, Cluster, IBEX, Voyager, Geotail, Wind and many others, and >120 Ground-Based investigations. SPDF recently added support for ARASE/ERG and MAVEN as supplementary access at the requests of those missions, and is expecting Solar Orbiter and ICON data. SPDF also operates the multi-mission orbit displays and query services of SSCWeb and the Java-based 4D Orbit Viewer, as well as the Heliophysics Data Portal (HDP) discipline-wide data inventory and access service, and OMNIWeb and COHOWeb for near-Earth and deep-space solar wind plasma, magnetic field, and energetic particle database, respectively.

The Adler Planetarium’s Teen Programs engages hundreds of young people in authentic STEM experiences each year. Several of our programs focus on artificial light at night (ALAN), a critical environmental, health, ecological and astronomical issue. Our work in ALAN Community Advocacy and Education began in 2015 through Youth Organization for Lights Out (YOLO), a bilingual program in English and Spanish based in Chicago’s predominantly Mexican and Mexican-American Little Village neighborhood. YOLO program participants use tools to collect and analyze light pollution data, attend field trips to local and state dark sky sites, facilitate telescope viewing at the Adler and in their community, and develop prototype solutions and action plans to increase awareness of light pollution’s local effects in Chicago. Programs focused on Instrumentation and Research grew out of the Adler’s educational high-altitude ballooning program, Far Horizons. In 2018, Far Horizons astronomers and engineers began developing Mission NITELite (Night Imaging of Terrestrial Environments), a high altitude balloon-based light pollution mapping mission along with undergraduate interns. To complement NITELite, Far Horizons designed GONet (Ground Observing Network), a low-cost all-sky imaging system to measure sky quality at night as part of its Stratonauts teen program. In 2019, high school students helped design, test, and build 50 GONet units as a potential new standard for worldwide ALAN monitoring. In 2020, Adler teen interns are working with the Cook County Forest Preserves to quantify regional sky quality with GONets in support of an application for an Urban Night Sky Place designation from the International Dark-Sky Association. Reflecting the collaborative nature of science, teens in the Instrumentation and Research programs partner closely with peers in the Community Advocacy and Education programs, learning from one another’s perspective while undertaking joint projects.

The occurrence of Jovian decametric emission (DAM) is sporadic as observed from ground-based instruments. When the timing intervals of observed occurrences of Jovian DAM are compared to all periods when Jupiter was observable, a set of Jovian DAM emission occurrence probabilities can be created. These probabilities are usefully plotted as a function of Jovian system III (magnetospheric) central meridian longitude (CML-III) and Io’s phase measured from superior geocentric conjunction (SGC), producing a CML-Io phase plane. It has been known since 1964 that Jovian DAM tends to have higher occurrence probabilities in different regions of the CML-Io phase plane, leading to the identification of different Io-related and non-Io-related DAM components. AJ4CO Observatory, located in High Springs, Florida, USA, has been observing Jupiter when it is within ~4.5 hours of transit since October, 2013. The primary instrument used for observing Jovian DAM is a swept-frequency (16 to 32 MHz) dual polarization spectrograph fed by an eight-element phased array of terminated folded dipoles. A high-speed digital spectrograph with a tunable 2 MHz bandwidth was also used from 2013 to 2016 to observe emission at higher time resolution. We analyze the dynamic spectra of Jovian DAM observed at AJ4CO Observatory from 2013 through 2020 to measure emission timing intervals and classify the emission into four types: L (for wideband L bursts), S (for wideband S bursts), N (for narrowband continuous emission), and T (for narrowband trains of S bursts). For this presentation, we show CML-Io phase plane probabilities categorized by radio frequency, polarization, emission type, and emission arc shape. We show how the various high-probability DAM regions within the phase plane change with each parameter and with various combinations of parameters. We present updated definitions of the DAM component phase plane boundaries and discuss how the DAM components appearing in various parts of the CML-Io phase plane may differ from one another.

Scientific expeditions can be used to engage the public in science learning within an exciting and compelling context, giving insight into the often messy and serendipitous nature of science and the humanness of scientists. The 2019-2020 MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) research expedition allowed scientists from around the world to study all aspects of the Arctic climate system at ground zero for a full seasonal cycle. The German icebreaker Polarstern was intentionally frozen in Arctic sea ice and drifted almost continuously across the Arctic Ocean for a year. The Polarstern served as the central research observatory for MOSAiC, allowing scientists to study Arctic sea ice, atmospheric processes, and more. Here we present our U.S.-based MOSAiC expedition outreach efforts and the role of MOSAiC scientists in them. By harnessing the public’s fascination with the Arctic and the excitement of the expedition, coordinated MOSAiC communication, outreach, and education efforts promoted a broad understanding of the changing Arctic and the societal implications of these changes, hopefully inspiring a future generation of potential scientists. Many MOSAiC scientists were directly engaged in MOSAiC outreach efforts with students, teachers, and the public, and outreach materials were developed that could be accessed and distributed virtually. The expedition was brought to life for learners of all ages by providing them with immersive experiences like VR Google Expeditions and 360-degree videos from the field, opportunities to connect directly with scientists through video calls and an #askmosaic question submission campaign, and insight into what day-to-day life on an icebreaker in the remote Arctic is like. Our outreach efforts helped us better understand the importance of providing scientists with diverse outreach opportunities that are fulfilling to them and the power in using scientific expeditions to engage the public.

Accurate photoionization rates are vital for the study and understanding of planetary ionospheres. Previous model calculations of terrestrial photoionization rates lack sufficient spectral resolution to account for highly structured photoionization cross sections as well as the solar spectral irradiance. We present new photoionization rate calculations from CPI’s Atmospheric Ultraviolet Radiance Integrated Code [AURIC; Strickland et al., 1999] using high-resolution (0.01 Å) solar spectra and high-resolution (0.01 Å) atomic oxygen (O) and molecular nitrogen (N2) photoionization cross sections. Theoretical photoionization cross sections of O are determined utilizing the R-matrix plus pseudo-states (RMPS) approximation whereas N2 cross sections are determined using the R-matrix approximation. We include 34 high-resolution partial O state photoionization cross sections and 3 high-resolution partial N2 state photoionization cross sections with supplemental Conway [1988] tabulations for molecular oxygen and the remaining N2 states. We find that photoionization rates computed at 0.01 Å resolution differ substantially from rates computed using low-resolution cross sections, especially in the lower thermosphere below 200 km. Specifically, we find that ionization production rate ratios exhibit variations in altitude of more than ±40% between the high- and low-resolution cases. Past low-resolution calculations at various low spectral resolutions do not sufficiently account for or preserve the highly structured auto-ionization lines in the photoionization cross sections [Meier et al., 2007]. These features, in combination with high-resolution solar spectra, allow photons to penetrate deeper into the Earth’s atmosphere producing larger total ionization rates. These higher ionization rates may finally resolve data-model discrepancies in altitude profiles of electron densities due to the use of low-resolution photoionization cross sections in current E-region models.

Managing river temperature in highly urbanized stream systems is critical for maintaining aquatic ecosystems and associated beneficial uses. Elevated river temperatures arise from warm surface inflows from impervious areas, channelization, the absence of riparian forests, and the lack of groundwater-surface water interactions. In the current work, we utilize a mechanistic river temperature model, i-Tree Cool River, to evaluate the cooling impacts of alternative ecological restoration scenarios: a) shading effects of tree planting in riparian areas and b) alternative streambed materials. The model was calibrated and validated on a 4.2 km reach of the Compton Creek in the Los Angeles (LA) River watershed, California, for low and high flow periods. The Arroyo Chub and Stickleback were considered the target species for analyzing thermal habitat suitability. River temperature simulations showed that like the ambient air temperature. The thermal response of the river in high flow periods was a function of upstream river temperature , where in low flow periods river water temperature was most affected by the tested restoration scenarios. Tree planting in the riparian corridor decreased the median thermal metrics: Max Weekly Max, Max Weekly Average, and Min Weekly Min Temperatures by an average of 3 ℃ (13%) to 20.4 ℃, 19.7 ℃, and 17.8 ℃, respectively. Using limecrete as an alternative bed material to the current concrete bottom decreased the median thermal metrics by an average of 0.9 ℃ (4%) to 22.7 ℃, 22 ℃, and 19 ℃, respectively. Combining the two scenarios decreased the river temperature metrics by an average of 4 ℃ (18%) to 18.2 ℃. Besides riparian vegetation, altering bed material is an impactful option in case of groundwater contamination and if channelized urban corridors lack the substrate to support vegetation. The use of ecological restoration scenarios resulted in summertime temperatures were within the documented spawning temperature thresholds and therefore temperature would not be a limiting factor in the potential reintroduction of the Arroyo Chub and Stickleback to Compton Creek. This tributary could be considered as a potential refuge and improved fish habitat in the LA basin during low flow periods.

The geospatial Imaging Spectroscopy Processing Environment on the Cloud (ImgSPEC; formerly GeoSPEC) pioneers an on-demand science data processing system (SDPS) producing user-customized Level 1 calibrated radiance to Level 3+ data products in anticipation for the 2017-2027 Earth Decadal Survey prioritized spaceborne global imaging spectrometer to advance the study of Surface Biology and Geology (SBG). SBG data volumes (~20 TB/day) of high dimensionality (>224 bands) would be infeasible to download and the breadth of applications of the data across dozens of disciplines presents a need to evolve the traditional NASA SDPS. ImgSPEC streamlines processing data into key SBG observables that have demonstrated algorithms at local-to-regional scales and may vary locally. As such, a traditional, monolithic SDPS could not fully exploit the information in SBG measurements. To remove this barrier to use, ImgSPEC demonstrates an on-demand SDPS prototype that improves imaging spectroscopy data discovery, access, and utility enabling shared knowledge transfer from advanced imaging spectroscopy users to less experienced users such as decision makers and the general public. We test three use cases: 1) standard data processing workflows, 2) customized variants of standard workflows, and 3) algorithm development of new workflows. We create collaborative algorithm development environments that offer services typically restricted to NASA SDPSs such as data product provenance and bulk processing. We leverage existing NASA-funded information technologies such as the hybrid on-premise/ cloud science data system (HySDS), the Multi-mission Algorithm and Analysis Platform (MAAP), ECOSIS – a crowd-sourced spectral database, and ECOSML – a crowd-sourced model database. We demonstrate ImgSPEC on the Terrestrial Ecosystem use case processing through to foliar traits and fractional cover, thus aligning with driving thrusts for the SBG Science and Applications Communities.

Urban expansion is a global trend primarily determined by overpopulation, particularly in developing countries like India. The pattern and boundary of urban expansion could be observed and modelled on a Spatio-temporal dimension in the Asan watershed, situated in western Doon Valley, Uttarakhand, India. The specific objectives of the present study are: 1) Monitoring Land Use Land Cover (LULC) change detection and its impact on the hydrological regime of the watershed, 2) Generation of urban growth pattern map to identify the urban expansion along with the directions and distance from the watershed centroid. To achieve the objectives, the four LULC maps of different years are generated through Landsat and LISS-IV dataset from 1980 to 2016. The eight classified classes of LULC includes: build-up (urban and rural), cropland, fallow land, plantation, evergreen needle leaf forest, deciduous broadleaf forest, mixed forest, and water bodies. The LULC change analysis concludes that the built-up area continuously expanding at an alarming rate, nearly four folds in the last 36 years. The major areal portion of cropland and plantation are converted into build-up at a faster rate after 1995 whereas the areal extent of water bodies are also reduced. Based on monitoring of urban growth, the wind rose tool revealed that the southwest direction is the dominant direction of the urban expansion till 1980 and 1980-1995 which contribute 48% and 47.61% respectively of the total urban area of the watershed. However, in the period 1995-2008 and 2008-2016, the eastern direction is predominant, which contribute 36.44% and 35.27% respectively from the total urban areal expansion. This reflects a situation where the eastern direction is expected to dominate in the next years. Consequences of urban expansion create pressure on the into the watershed which leads to degradation of natural resources, forest environment and hydrological elements of the watershed.

The Environment-Vulnerability-Decision-Technology (EVDT) integrated modeling framework considers the interactions between the environment, societal impact, human decision-making, and technology design to support decision making. EVDT has been expanded to include a public health model in the Vida Decision Support System, which will help local leaders understand the relationships between societal factors relating to COVID-19. Key to the development of Vida are collaborative design and mutual learning with international and interdisciplinary teams. Collaborations with researchers and government officials (including public health, economics, environmental, and demographic data collection officials) in Angola, Brazil, Chile, Indonesia, Mexico and the United States provide in-depth understanding of local contexts. Lessons learned from these collaborations include the value of dialogues with teams from the same region but different topic areas (such as a space agency compared to a public health agency), allowing for time to learn the best way to combine diverse data types and find the tools each collaborator prefers, and encouraging the use of the preferred language of collaborators. During Vida’s development, each collaborator has worked to create their own version of Vida using local data sources, the US team has provided prototype analyses and models, and collaborators have shared individual insights among the whole network. These partnerships have yielded promising initial results to support decision making, with prototype tools incorporating local data on COVID cases, the environment, and socio-economic factors from Rio De Janeiro and Chile being evaluated. This collaborative design process will develop insights for decision-making, create a network of international collaborators that can exchange technical methods beyond the pandemic, and emphasize the principles of inclusive innovation and decoloniality by submitting to the preferences of local leaders in each country.

Subduction zone earthquakes show varying energy release patterns and frequency content, based on their tectonic settings and hypocentral depths. Resolving these features from the nonlinear and non-stationary seismograms is a challenge. Our work in the Japan Trench follows studies by Huang et al. (1998, 2001) and Zhang et al. (2003), who demonstrated the use of empirical mode decomposition to separate records into multiple timescales, or intrinsic mode functions (IMFs). Zhang et al. observed that IMFs 2-5 represented the source rupture process for the 1994 Northridge earthquake. Chauhan (master’s thesis, 2019) used time-frequency distributions, short-time Fourier and continuous wavelet transforms, of IMFs of strong-motion data for a pair of interplate-intraslab earthquakes to identify the dominant, short duration, low-frequency energy release for the intraslab event. He found a high correlation between the original signal and a linear combination of IMFs 3 and 4, possibly representing the source. Chatterjee et al. (AGU, 2018) observed an association between time-frequency-energy distributions of certain IMFs and moment rate functions (MRFs) from teleseismic waveform models, for five earthquakes. Chatterjee et al. (AGU, 2019) and Mache et al. (AGU, 2019) used Hilbert spectral analysis (Huang et al., 1998) of IMFs selected based on their frequency and energy and observed better match between the two. This new function, which they regard as the Energy Rate Function (ERF), can reproduce the MRF’s essential elements, i.e., its duration and shape, but Mache (master’s thesis, 2020) observed that results depended on the selection of stations. As the next step, Mache and Rajendran (JpGU-AGU, 2020) based the selection criteria on the slip distribution, strike, and JMA intensity distribution maps (JMA 1996) and applied the method to 7 earthquakes from various tectonic settings of the Japan Trench. Here we present an overview of the various methods for analyzing KiK-net strong-motion data for selected earthquakes to extract information on their time-frequency-energy distributions. The ERF generated through this analysis is a physically compatible expression of the MRF and, therefore, more useful in predicting the shaking effects of earthquakes.

Conventionally, radar-based nowcasting methods have been conducted by shifting or extrapolating the most recent estimated precipitation field along the estimated motion field to lead times up to 3 hr. However, such methods assume that these fields do not evolve during the lead-time period. A more recent nowcasting approach is the short-term ensemble prediction system known as STEPS. STEPS aims to filter small spatial scales of rain as they have an expected short life-time compared to large spatial scales. Besides, it perturbs the precipitation field during extrapolation so that the prediction uncertainties related to the evolution of precipitation is considered, resulting in an ensemble nowcast. In this work, the configuration, implementation, and performance of STEPS are studied for its radar-based ensemble nowcasting application in Germany. Attention is given to the spatial localization (i.e., the adjustment of parameters that control the spatio-temporal evolution of precipitation in large domains). For such purpose, the capability of STEPS is analyzed using multiple rain events collected by the German radar network. Preliminary analysis regarding the spatial scale filtering of STEPS (without perturbation) shows an improvement against conventional extrapolation of 20 to 30 % on the critical success index starting at lead times of 30 min at a rainfall threshold of 0.1 mm hr-1. In terms of the spatial structure, STEPS (without perturbation) provides a consistent nowcast for lead times up to 3 hr. Although small spatial structures are filtered, the spatial structure at scales of 32 km or larger is maintained. The skill of a probabilistic nowcast (STEPS with perturbation) was also analyzed. It is shown that for the probability of exceeding the 5.0 mm hr-1 threshold, the predictability of the nowcast is compromised for lead times on the order of 1 hr. However, the capability of identifying rain and non-rain areas is still valuable: the hit rates are still larger than the false alarm rates. Our preliminary results highlight aspects needed in the configuration of STEPS such as the evolution model and the perturbation model. In this manner, this study can serve as a basis for an improved nowcasting system in Germany and as a reference for forecasters to understand the characteristics of the examined nowcast system.

Extreme precipitation events from the western disturbances have significant impacts on the water management and ecosystem services in the Upper Indus basin, a part of the Hindukush Himalayan region. Further, there are changes in the duration, intensity, spatial extent and frequency of extreme events due to climate change. In this domain, a few studies have assessed the impact of climate change in the study area with limited data. There will be high uncertainty in the outcomes obtained from the investigation of extreme events with limited data. Therefore, a comprehensive analysis of extreme precipitation events from western disturbances considering high resolution with long-term data is required in the study area. Accordingly, In the present study, Precipitation based ETCCDI Indices are calculated for every year, and non-parametric Mann-Kendall test is applied Sen slope is calculated to detect the changes in the monthly precipitation during the winter season for the period 1901-2019. The findings of the present study reveal the northwest region has an increasing trend in RX1day extreme precipitation of 1.85 mm per decade in December, and the rate has amplified due to the effect of western disturbances in January and February. Also, the pattern of RX5day extreme precipitation is consistent in January and February. Overall the wetness in increasing over the north-west part of the study area In recent decades, the northwest region of the upper Indus basin had faced more extreme events with severe impacts due to western disturbances, and outcomes from the study can improve the understanding of extreme events.

Use of ensemble rainfall forecasts has gained popularity in providing detailed uncertainty information and improving hydrologic prediction skill. India Meteorological Department (IMD) provides medium-range multi-model ensemble (MME) forecasts for all over India with 1- to 5-day lead time. In this study, we bias correct the IMD MME rainfall forecasts using a modified version of Kohonen Self-Organizing Maps (KSOM) and analyse the effect of bias correction on the streamflow prediction skill of MIKE 11 Hydrodynamic (HD) model for the years 2012-2014. We have selected the upper region of the Mahanadi River basin as the test bed. The results indicate improvement of rainfall forecasts after bias correction. Subsequently, use of bias corrected rainfall forecasts as input forcing to the MIKE 11 HD model provides better streamflow forecasts at all the lead times (Nash Sutcliffe Efficiency, NSE ranging from 0.89 to 0.41) compared to the use of raw forecasts (NSE ranging from 0.89 to -0.51). To further improve the streamflow forecasts, we have applied the recently developed robust wavelet-based non-linear autoregressive with exogenous inputs dynamic neural network model, WNARX. This post-processing operation tends to improve the streamflow forecast quality to an acceptable range (NSE = 0.92-0.71). The results encourage us to conclude that the IMD MME forecasts has great potential to improve streamflow prediction skill of a hydrodynamic model.