Ancillary science with Ariel: Feasibility and scientific potential of young stellar object observations

To investigate the feasibility of ancillary target observations with ESA's Ariel mission, we compiled a list of potentially interesting young stars: FUors, systems harbouring extreme debris discs and a larger sample of young stellar objects showing strong near/mid-infrared excess. These objects can be observed as additional targets in the waiting times between the scheduled exoplanet transit and occultation observations. After analyzing the schedule for Ariel an algorithm was constructed to find the optimal target to be observed in each gap. The selection was mainly based on the slew and stabilization time needed to observe the selected YSO, but it also incorporated the scientific importance of the targets and whether they have already been sufficiently measured. After acquiring an adequately large sample of simulation data, it was concluded that approximately 99.2% of the available -- at least one hour long -- gaps could be used effectively. With an average slewing and stabilization time of about 16.7 minutes between scheduled exoplanet transits and ancillary targets, this corresponds to an additional $2881 \pm 56$ hours of active data gathering. When this additional time is used to observe our selected 200 ancillary targets, a typical signal-to-noise ratio of $\sim$10$^4$ can be achieved along the whole spectral window covered by Ariel.Comment: Accepted for publication in Experimental Astronom

Global integrated drought monitoring and prediction system

Drought is by far the most costly natural disaster that can lead to widespread impacts, including water and food crises. Here we present data sets available from the Global Integrated Drought Monitoring and Prediction System (GIDMaPS), which provides drought information based on multiple drought indicators. The system provides meteorological and agricultural drought information based on multiple satellite-, and model-based precipitation and soil moisture data sets. GIDMaPS includes a near real-time monitoring component and a seasonal probabilistic prediction module. The data sets include historical drought severity data from the monitoring component, and probabilistic seasonal forecasts from the prediction module. The probabilistic forecasts provide essential information for early warning, taking preventive measures, and planning mitigation strategies. GIDMaPS data sets are a significant extension to current capabilities and data sets for global drought assessment and early warning. The presented data sets would be instrumental in reducing drought impacts especially in developing countries. Our results indicate that GIDMaPS data sets reliably captured several major droughts from across the globe

SSI_NLDAS.zip

<p>The Global Integrated Drought Monitoring and Prediction System (GIDMaPS) provides near real-time drought information based on multiple drought indicators and input data sets (http://drought.eng.uci.edu/). In this package, the folder names indicate the drought index and the source of data (INDEX_SOURCE). Index options are: 1-PSI: Multivariate Standardized Drought Index (MSDI) based on Precipitation (P) and Soil Moisture (S). 2-SSI: Standardized Soil Moisture Index 3-SPI: Standardized Precipitation Index. In each folder the files are named as INDEXYYYYMM.asc, where YYYY= year; MM= month, and INDEX = PSI, SSI, SPI.</p> <p><br></p> <p>GIDMaPS is presented in [Hao et al, 2014]. The deatails of MSDI, SSI and SPI are discussed in [Hao and AghaKouchak 2013a, 2013b]. GIDMaPS data has been used in [Damberg and AghaKouchak, 2013].</p> <p> </p> <p><strong>References</strong></p> <p>[1] Hao Z., AghaKouchak A., Nakhjiri N., Farahmand A., 2014, Global Integrated Drought Monitoring and Prediction System, Scientific Data, 1: 140001, doi: 10.1038/sdata.2014.1.</p> <p><br></p> <p>[2]  Hao Z., AghaKouchak A., 2013a, A Nonparametric Multivariate Multi-Index Drought Monitoring Framework,Journal of Hydrometeorology, doi:10.1175/JHM-D-12-0160.1.</p> <p><br></p> <p>[3]  Hao Z., AghaKouchak A., 2013b, Multivariate Standardized Drought Index: A Parametric Multi-Index Model,Advances in Water Resources, 57, 12-18, doi: 10.1016/j.advwatres.2013.03.009.</p> <p><br></p> <p>[4]  Damberg L., AghaKouchak A., 2013, Global Trends and Patterns of Droughts from Space, Theoretical and Applied Climatology, doi: 10.1007/s00704-013-1019-5. </p> <p><br></p

PSI_MERRA.zip

<p>The Global Integrated Drought Monitoring and Prediction System (GIDMaPS) provides near real-time drought information based on multiple drought indicators and input data sets (http://drought.eng.uci.edu/). In this package, the folder names indicate the drought index and the source of data (INDEX_SOURCE). Index options are: 1-PSI: Multivariate Standardized Drought Index (MSDI) based on Precipitation (P) and Soil Moisture (S). 2-SSI: Standardized Soil Moisture Index 3-SPI: Standardized Precipitation Index. In each folder the files are named as INDEXYYYYMM.asc, where YYYY= year; MM= month, and INDEX = PSI, SSI, SPI.</p> <p><br></p> <p>GIDMaPS is presented in [Hao et al, 2014]. The deatails of MSDI, SSI and SPI are discussed in [Hao and AghaKouchak 2013a, 2013b]. GIDMaPS data has been used in [Damberg and AghaKouchak, 2013].</p> <p> </p> <p><strong>References</strong></p> <p>[1] Hao Z., AghaKouchak A., Nakhjiri N., Farahmand A., 2014, Global Integrated Drought Monitoring and Prediction System, Scientific Data, 1: 140001, doi: 10.1038/sdata.2014.1.</p> <p><br></p> <p>[2]  Hao Z., AghaKouchak A., 2013a, A Nonparametric Multivariate Multi-Index Drought Monitoring Framework,Journal of Hydrometeorology, doi:10.1175/JHM-D-12-0160.1.</p> <p><br></p> <p>[3]  Hao Z., AghaKouchak A., 2013b, Multivariate Standardized Drought Index: A Parametric Multi-Index Model,Advances in Water Resources, 57, 12-18, doi: 10.1016/j.advwatres.2013.03.009.</p> <p><br></p> <p>[4]  Damberg L., AghaKouchak A., 2013, Global Trends and Patterns of Droughts from Space, Theoretical and Applied Climatology, doi: 10.1007/s00704-013-1019-5. </p> <p><br></p

PSI_MERRA_GIDMaPS

<p>Drought Index: MSDI,</p> <p>Source: MERRA</p

PSI_GLDAS.zip

<p>The Global Integrated Drought Monitoring and Prediction System (GIDMaPS) provides near real-time drought information based on multiple drought indicators and input data sets (http://drought.eng.uci.edu/). In this package, the folder names indicate the drought index and the source of data (INDEX_SOURCE). Index options are: 1-PSI: Multivariate Standardized Drought Index (MSDI) based on Precipitation (P) and Soil Moisture (S). 2-SSI: Standardized Soil Moisture Index 3-SPI: Standardized Precipitation Index. In each folder the files are named as INDEXYYYYMM.asc, where YYYY= year; MM= month, and INDEX = PSI, SSI, SPI.</p> <p><br></p> <p>GIDMaPS is presented in [Hao et al, 2014]. The deatails of MSDI, SSI and SPI are discussed in [Hao and AghaKouchak 2013a, 2013b]. GIDMaPS data has been used in [Damberg and AghaKouchak, 2013].</p> <p> </p> <p><strong>References</strong></p> <p>[1] Hao Z., AghaKouchak A., Nakhjiri N., Farahmand A., 2014, Global Integrated Drought Monitoring and Prediction System, Scientific Data, 1: 140001, doi: 10.1038/sdata.2014.1.</p> <p><br></p> <p>[2]  Hao Z., AghaKouchak A., 2013a, A Nonparametric Multivariate Multi-Index Drought Monitoring Framework,Journal of Hydrometeorology, doi:10.1175/JHM-D-12-0160.1.</p> <p><br></p> <p>[3]  Hao Z., AghaKouchak A., 2013b, Multivariate Standardized Drought Index: A Parametric Multi-Index Model,Advances in Water Resources, 57, 12-18, doi: 10.1016/j.advwatres.2013.03.009.</p> <p><br></p> <p>[4]  Damberg L., AghaKouchak A., 2013, Global Trends and Patterns of Droughts from Space, Theoretical and Applied Climatology, doi: 10.1007/s00704-013-1019-5. </p> <p><br></p

SPI_NLDAS.zip

<p>The Global Integrated Drought Monitoring and Prediction System (GIDMaPS) provides near real-time drought information based on multiple drought indicators and input data sets (http://drought.eng.uci.edu/). In this package, the folder names indicate the drought index and the source of data (INDEX_SOURCE). Index options are: 1-PSI: Multivariate Standardized Drought Index (MSDI) based on Precipitation (P) and Soil Moisture (S). 2-SSI: Standardized Soil Moisture Index 3-SPI: Standardized Precipitation Index. In each folder the files are named as INDEXYYYYMM.asc, where YYYY= year; MM= month, and INDEX = PSI, SSI, SPI.</p> <p><br></p> <p>GIDMaPS is presented in [Hao et al, 2014]. The deatails of MSDI, SSI and SPI are discussed in [Hao and AghaKouchak 2013a, 2013b]. GIDMaPS data has been used in [Damberg and AghaKouchak, 2013].</p> <p> </p> <p><strong>References</strong></p> <p>[1] Hao Z., AghaKouchak A., Nakhjiri N., Farahmand A., 2014, Global Integrated Drought Monitoring and Prediction System, Scientific Data, 1: 140001, doi: 10.1038/sdata.2014.1.</p> <p><br></p> <p>[2]  Hao Z., AghaKouchak A., 2013a, A Nonparametric Multivariate Multi-Index Drought Monitoring Framework,Journal of Hydrometeorology, doi:10.1175/JHM-D-12-0160.1.</p> <p><br></p> <p>[3]  Hao Z., AghaKouchak A., 2013b, Multivariate Standardized Drought Index: A Parametric Multi-Index Model,Advances in Water Resources, 57, 12-18, doi: 10.1016/j.advwatres.2013.03.009.</p> <p><br></p> <p>[4]  Damberg L., AghaKouchak A., 2013, Global Trends and Patterns of Droughts from Space, Theoretical and Applied Climatology, doi: 10.1007/s00704-013-1019-5. </p> <p><br></p

SSI_MERRA_GIDMaPS

<p>Drought Index: SSI,</p> <p>Source: MERRA</p

PSI_NLDAS.zip

<p>The Global Integrated Drought Monitoring and Prediction System (GIDMaPS) provides near real-time drought information based on multiple drought indicators and input data sets (http://drought.eng.uci.edu/). In this package, the folder names indicate the drought index and the source of data (INDEX_SOURCE). Index options are: 1-PSI: Multivariate Standardized Drought Index (MSDI) based on Precipitation (P) and Soil Moisture (S). 2-SSI: Standardized Soil Moisture Index 3-SPI: Standardized Precipitation Index. In each folder the files are named as INDEXYYYYMM.asc, where YYYY= year; MM= month, and INDEX = PSI, SSI, SPI.</p> <p><br></p> <p>GIDMaPS is presented in [Hao et al, 2014]. The deatails of MSDI, SSI and SPI are discussed in [Hao and AghaKouchak 2013a, 2013b]. GIDMaPS data has been used in [Damberg and AghaKouchak, 2013].</p> <p> </p> <p><strong>References</strong></p> <p>[1] Hao Z., AghaKouchak A., Nakhjiri N., Farahmand A., 2014, Global Integrated Drought Monitoring and Prediction System, Scientific Data, 1: 140001, doi: 10.1038/sdata.2014.1.</p> <p><br></p> <p>[2]  Hao Z., AghaKouchak A., 2013a, A Nonparametric Multivariate Multi-Index Drought Monitoring Framework,Journal of Hydrometeorology, doi:10.1175/JHM-D-12-0160.1.</p> <p><br></p> <p>[3]  Hao Z., AghaKouchak A., 2013b, Multivariate Standardized Drought Index: A Parametric Multi-Index Model,Advances in Water Resources, 57, 12-18, doi: 10.1016/j.advwatres.2013.03.009.</p> <p><br></p> <p>[4]  Damberg L., AghaKouchak A., 2013, Global Trends and Patterns of Droughts from Space, Theoretical and Applied Climatology, doi: 10.1007/s00704-013-1019-5. </p> <p><br></p