Radiation measurements from polar and geosynchronous satellites

Nimbus 5 satellite data and its use to study climate and develop climate model experiments are examined. Data cover: (1) physical basis for understanding long range and climate monitoring and prediction problem, (2) delineating energy loss to space between contributions from land, ocean, and atmosphere, (3) temporal and spatial distribution in earth's energy budget and its variations due to global cloud fields, and (4) studies of the frequency of occurrence of precipitation over ocean areas

Third National Aeronautics and Space Administration Weather and climate program science review

Research results of developing experimental and prototype operational systems, sensors, and space facilities for monitoring, and understanding the atmosphere are reported. Major aspects include: (1) detection, monitoring, and prediction of severe storms; (2) improvement of global forecasting; and (3) monitoring and prediction of climate change

TriG - A GNSS Precise Orbit and Radio Occultation Space Receiver

The GPS radio occultation (RO) technique [1] produces measurements in the ionosphere and neutral atmosphere [2] that contribute to monitoring space weather and climate change; and improving operational weather prediction. The high accuracy of RO soundings, traceable to SI standards, makes them ideal climate benchmark observations. For weather applications, RO observations improve the accuracy of weather forecasts by providing temperature and moisture profiles of sub-km vertical resolution, over land and ocean and in the presence of clouds. JPL is currently flying a handful of RO instruments [3] on various satellites in Low Earth Orbit (LEO). Although these receivers have served to pioneer occultation measurements, various advances in technology and understanding of the RO technique along with availability of new signals from GPS and other GNSS satellites allow us to design an improved next generation space-based Precise Orbit Determination (POD) and RO receiver, the TriG receiver. The paper describes the architecture and implementation of the JPL TriG receiver as well as results obtained with a prototype receiver demonstrating key technologies necessary for a next-generation space science receiver

WMO Space-Based Weather and Climate Extremes Monitoring Demonstration Project (SEMDP): First Outcomes of Regional Cooperation on Drought and Heavy Precipitation Monitoring for Australia and Southeast Asia

To improve monitoring of extreme weather and climate events from space, the World Meteorological Organization (WMO) initiated the space-based weather and climate extremes monitoring demonstration project (SEMDP). Presently, SEMDP is focused on drought and heavy precipitation monitoring over Southeast Asia and the Pacific. Space-based data and derived products form critical part of meteorological services’ operations for weather monitoring; however, satellite products are still not fully utilized for climate applications. Using SEMDP satellite-derived precipitation products, it would be possible to monitor extreme precipitation events with uniform spatial coverage and over various time periods – pentad, weekly, 10 days, monthly and longer time-scales. In this chapter, SEMDP satellite-derived precipitation products over the Asia-Pacific region produced by the Earth Observation Research Center/Japan Aerospace Exploration Agency (EORC/JAXA) and the Climate Prediction Center/National Oceanic and Atmospheric Administration (CPC/NOAA) are introduced. Case studies for monitoring (i) drought in Australia in July-October 2007 and September 2018 and (ii) heavy precipitation over Australia in December 2010 and Thailand and the Peninsular Malaysia in November-December 2014 which caused widespread flooding are also presented. Satellite observations are compared with in situ data to demonstrate value of satellite-derived estimates of precipitation for drought and heavy rainfall monitoring

Scaling-up climate services with users in Latin America

Latin America farmers are highly vulnerable to climate variability, with crop losses observed throughout the region on a virtually annual basis. For instance, as indicated by the United Nations’ Food and Agriculture Organization (FAO) and World Food Program (WFP), the 2014–2017 drought conditions in Central America affected over 3.5 million people in Guatemala, Honduras and El Salvador. At the same time, local stakeholders and farmers generally have limited access to existing climate and forecast information, do not have sufficient capacities to understand the climate information and/or mechanisms to relate this information to the impact that climate variations can generate at a local level. This precludes the translation of information into actionable knowledge, and therefore into action. In this study, we describe a process through which scientists and strategic partners have co-developed, tested and scaled out an approach to assess, co-produce, translate and transfer climate information to enable agricultural decision making –the Local Technical Agroclimatic Committees (LTAC). LTACs allow open and clear dialogues about climate variations at multiple timescales, how these can affect crops, and the design of measures to reduce crop loss, particularly providing agronomic recommendations to farmers. We systematically describe the process of evidence generation, creation, partner engagement, scaling up, and monitoring of the approach throughout Latin America. Currently, 35 LTACs exist in 9 Latin American countries, engaging more than 250 public and private institutions, increasing the resilience and food security of an estimated 330,000 farmers, and potentially transforming how Latin American farmers manage climate risk. The study illustrates changes in institutional and farmers' capacities to co-produce, translate and use climate information and explores how better climate and crop prediction models can effectively underpin this process. We show how strategic alliances with farmer organizations, national public, and private and regional climate outlook forums help deliver improved and accurate climate information to users. Finally, we document how LTACs and their integration with other local-scale processes have led to changes in farmers’ management practices to take better advantage of good climatic conditions or avoid losses

Spatial interpolation of high-frequency monitoring data

Climate modelers generally require meteorological information on regular grids, but monitoring stations are, in practice, sited irregularly. Thus, there is a need to produce public data records that interpolate available data to a high density grid, which can then be used to generate meteorological maps at a broad range of spatial and temporal scales. In addition to point predictions, quantifications of uncertainty are also needed. One way to accomplish this is to provide multiple simulations of the relevant meteorological quantities conditional on the observed data taking into account the various uncertainties in predicting a space-time process at locations with no monitoring data. Using a high-quality dataset of minute-by-minute measurements of atmospheric pressure in north-central Oklahoma, this work describes a statistical approach to carrying out these conditional simulations. Based on observations at 11 stations, conditional simulations were produced at two other sites with monitoring stations. The resulting point predictions are very accurate and the multiple simulations produce well-calibrated prediction uncertainties for temporal changes in atmospheric pressure but are substantially overconservative for the uncertainties in the predictions of (undifferenced) pressure.Comment: Published in at http://dx.doi.org/10.1214/08-AOAS208 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

NDVI Short-Term Forecasting Using Recurrent Neural Networks

In this paper predictions of the Normalized Difference Vegetation Index (NDVI) data recorded by satellites over Ventspils Municipality in Courland, Latvia are discussed. NDVI is an important variable for vegetation forecasting and management of various problems, such as climate change monitoring, energy usage monitoring, managing the consumption of natural resources, agricultural productivity monitoring, drought monitoring and forest fire detection. Artificial Neural Networks (ANN) are computational models and universal approximators, which are widely used for nonlinear, non-stationary and dynamical process modeling and forecasting. In this paper Elman Recurrent Neural Networks (ERNN) are used to make one-step-ahead prediction of univariate NDVI time series

Prediction model for chilli productivity based on climate and productivity data

The global trade increases the competition in agricultural product export all around the world.The Indonesian Agricultural Industry needs to improve their competitiveness by fulfilling the requirements and restrictions imposed by some countries with regards to traceability information features of the products, such as location of farming field cultivation method, chemical contaminants and supply chain information. Some European countries require the implementation of E-GAP (European Good Agricultural Practices) in order to secure food safety. Food safety provides the control and monitoring during, pre-, and post-harvest stages of agricultural products. This paper describes a prediction model based on the climate and productivity data on Indonesian agricultural products. The prediction model with an iteration of the climate and their possible increase or decrease in productivity. The model relies on historical data and an analytical algorithm. The decision support and early warning system provides the farmer some advice to reduce the crop failure risks due to climate chang

Long Short-Term Memory Approach for Predicting Air Temperature In Indonesia

Air temperature is one of the main factors for describing the weather behaviour in the earth. Since Indonesia is located on and near equator, then monitoring the air temperature is needed to determine either global climate change occurs or not. Climate change can have an impact on biological growth in various fields. For instance, climate change can affect the quality of production and growth of animal and plants. Therefore, air temperature prediction is important to meteorologists and Indonesian government to provide information in many sectors. Various prediction algorithms have been used to predict temperature and produce different accuracy. In this study, the deep learning method with Long Short-Term Memory (LSTM) model is used to predict air temperature. Here, the results show that LSTM model with one layer and Adaptive Moment Estimation (ADAM) optimizer produce accuracy which is 32% of , 0.068 of MAE and 0.99 of RMSE. Moreover, here, ADAM optimizer is found better than Stochastic Gradient Descent (SGD) optimizer

Phytoplankton Dynamics and Harmful Algal Species in the Potomac River Estuary

Phytoplankton populations are a primary driver of chemical and biological dynamics and are therefore important sentinel organisms for monitoring environmental perturbations. Additionally, long term ecological monitoring in the Potomac River estuary provides opportunities to examine phytoplankton dynamics. Annual blooms of the cyanoHAB Microcystis were observed in the 1970’s and 80’s, and since declined in frequency. A large Microcystis aeruginosa bloom occurred, summer 2011, prompting investigation of forecasting efforts for harmful algal species. Three prediction methods were investigated, with binary linear regression identified as the most appropriate forecasting tool. Coastal marine ecosystems are also at risk from climate change and phytoplankton provide a crucial monitoring tool. Extensive time series analysis revealed changes in phytoplankton phenology in response to climate indicators, mainly a shift in the timing of maximum abundance of diatoms and cryptophytes. It is likely that this change in phenology has an effect on energy transfer to higher trophic levels