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NASA NTRS2019

Definition of a Technology Validation Mission for P-band Reflectometry using Signals of Opportunity

Root-Zone Soil Moisture (RZSM) (moisture profile in the top meter of soil) and Snow Water Equivalent (SWE) (total snow pack water content) are identified as priority target variables in the ESAS 2017 decadal survey [1] with critical roles in hydrology and water management. RZSM estimates are vital for understanding multiple Earth system processes and forecasting (for example, droughts [2]). Simultaneous knowledge of surface and RZSM could enable a breakthrough in estimating key unobserved hydrologic fluxes and reduce uncertainty in net ecosystem exchange (NEE), carbon balance [3] discharge estimates, and crop yield forecasts [4] .With the high albedo and insulating properties of snow, monitoring, SWE accumulation would provide a key constraint on the potential runoff during spring ablation while monitoring SWE disappearance rates would provide a key constraint on SWE partition into runoff vs. infiltration/recharge. [5] demonstrated that knowledge of early-spring SWE generally contributes most to streamflow forecast skill in the Western U.S. SWE is also a source of water storage that provides the water resources during spring snowmelt. Despite such potentially transformative contributions, accurate RZSM and SWE measurements are unattainable with current technology. While active/passive L-band methods (e.g. SMAP, SMOS) can reliably retrieve surface soil moisture in the top 5 cm of soil [6], [7]. RZSM estimates are only available through model assimilation of brightness temperatures with a radiative transfer and land surface models [8]. SWE estimation uses multi-frequency passive microwave techniques (e.g. [9]-[11]), which have significant problems with deeper snow and in forested and mountainous environments [12]. Signals of opportunity (SoOp) in P-band (200-400 MHz) is a new remote sensing technique with the capability of estimating both essential hydrologic variables, RZSM and SWE, circumventing many of the aforementioned limitations under all weather conditions day and night. SoOp is the re-utilization of existing powerful satellite transmissions within bands allocated for communications or navigation. P-band SoOp sensitivity to soil moisture has been demonstrated in an airborne experiment over Oklahoma in 2016 [13]. Recent theory [14] and experiments [15] have also confirmed that the reflection coefficient phase is proportional to SWE.

Garrison, J. L.↗

NASA NTRS2007

Forecast of the Amplitude of Solar Cycle 24 Based on the Disturbed Days Precursor

R. J. Thompson (1993, Solar Physics 148, 383) exhibited a significant linear relationship between the number of geomagnetically disturbed days (those that exceed some threshold value of the Ap or nu index) in a solar cycle and the sum of the peak sunspot number in that cycle and the next cycle. Thus, the number of disturbed days during a full cycle, together with the peak sunspot number in that cycle, is a predictor of the amplitude of the next cycle. The work reported here applies Thompson's method to the current cycle. Linear relationships as described above are derived both for the pure aa record (1868-2006) and for a composite of aa (1868-1931) and Ap (1932-2006). For the composite record, the relationship between aa and Ap is determined cycle-by-cycle during the period of overlap. The method is tested for sensitivity to the adopted Ap (or equivalent aa) threshold. The highest smoothed monthly sunspot number for Cycle 24 is forecasted to be R(sub z) = 115 plus or minus 30, where the uncertainty is conservatively based on the full spread of the data around the fitted line in the sunspot number direction. In terms of smoothed monthly 10.7-cm radio flux, the forecast is F10.7 = 164 plus or minus 28.

Rabin, Douglas M.↗

NASA NTRS2010

Improving Forecast Skill by Assimilation of AIRS Temperature Soundings

AIRS was launched on EOS Aqua on May 4, 2002, together with AMSU-A and HSB, to form a next generation polar orbiting infrared and microwave atmospheric sounding system. The primary products of AIRS/AMSU-A are twice daily global fields of atmospheric temperature-humidity profiles, ozone profiles, sea/land surface skin temperature, and cloud related parameters including OLR. The AIRS Version 5 retrieval algorithm, is now being used operationally at the Goddard DISC in the routine generation of geophysical parameters derived from AIRS/AMSU data. A major innovation in Version 5 is the ability to generate case-by-case level-by-level error estimates delta T(p) for retrieved quantities and the use of these error estimates for Quality Control. We conducted a number of data assimilation experiments using the NASA GEOS-5 Data Assimilation System as a step toward finding an optimum balance of spatial coverage and sounding accuracy with regard to improving forecast skill. The model was run at a horizontal resolution of 0.5 deg. latitude X 0.67 deg longitude with 72 vertical levels. These experiments were run during four different seasons, each using a different year. The AIRS temperature profiles were presented to the GEOS-5 analysis as rawinsonde profiles, and the profile error estimates delta (p) were used as the uncertainty for each measurement in the data assimilation process. We compared forecasts analyses generated from the analyses done by assimilation of AIRS temperature profiles with three different sets of thresholds; Standard, Medium, and Tight. Assimilation of Quality Controlled AIRS temperature profiles significantly improve 5-7 day forecast skill compared to that obtained without the benefit of AIRS data in all of the cases studied. In addition, assimilation of Quality Controlled AIRS temperature soundings performs better than assimilation of AIRS observed radiances. Based on the experiments shown, Tight Quality Control of AIRS temperature profile performs best on the average from the perspective of improving Global 7 day forecast skill.

Susskind, Joel↗

NASA NTRS2013

The Impact of Model and Rainfall Forcing Errors on Characterizing Soil Moisture Uncertainty in Land Surface Modeling

The contribution of rainfall forcing errors relative to model (structural and parameter) uncertainty in the prediction of soil moisture is investigated by integrating the NASA Catchment Land Surface Model (CLSM), forced with hydro-meteorological data, in the Oklahoma region. Rainfall-forcing uncertainty is introduced using a stochastic error model that generates ensemble rainfall fields from satellite rainfall products. The ensemble satellite rain fields are propagated through CLSM to produce soil moisture ensembles. Errors in CLSM are modeled with two different approaches: either by perturbing model parameters (representing model parameter uncertainty) or by adding randomly generated noise (representing model structure and parameter uncertainty) to the model prognostic variables. Our findings highlight that the method currently used in the NASA GEOS-5 Land Data Assimilation System to perturb CLSM variables poorly describes the uncertainty in the predicted soil moisture, even when combined with rainfall model perturbations. On the other hand, by adding model parameter perturbations to rainfall forcing perturbations, a better characterization of uncertainty in soil moisture simulations is observed. Specifically, an analysis of the rank histograms shows that the most consistent ensemble of soil moisture is obtained by combining rainfall and model parameter perturbations. When rainfall forcing and model prognostic perturbations are added, the rank histogram shows a U-shape at the domain average scale, which corresponds to a lack of variability in the forecast ensemble. The more accurate estimation of the soil moisture prediction uncertainty obtained by combining rainfall and parameter perturbations is encouraging for the application of this approach in ensemble data assimilation systems.

Maggioni, V.↗

NASA NTRS1985

Analysis and forecast intercomparisons using the FGGE SOP-1 data base

The First GARP Global Experiment (FGGE) data base has provided the research community with the most complete global coverage of meteorological informaiton since measurements were first taken. It is important to understand the characteristics of the FGGE data base in terms of its accuracy and representativeness compared to the true state of the atmosphere. The uncertainty associated with the initial conditions used in a numerical model plays a significant role in subsequent error growth as shown by predictability theory. The ultimate limit of forecast skill is directly related to the initial uncertainty. The accuracy of analyses using FGGE data base coupled with the errors due to modeling imperfections determines the present skill of numerical models. An estimte of initial uncertainty is determined by examining differences in several FGGE analyses. The estimate is then used to determine an ultimate limit of forecast skill from derived predictability error growth rates. Several different calculations of predictability error growth are also compared. Finally, present-day forecast skill using FGGE analyses is contrasted to the estimates of ultimate skill, and the partition of model error versus initial uncertainty is discussed.

Baumhefner, D. P.↗

NASA NTRS2012

Translation of Land Surface Model Accuracy and Uncertainty into Coupled Land-Atmosphere Prediction

Land-atmosphere (L-A) Interactions playa critical role in determining the diurnal evolution of both planetary boundary layer (PBL) and land surface heat and moisture budgets, as well as controlling feedbacks with clouds and precipitation that lead to the persistence of dry and wet regimes. Recent efforts to quantify the strength of L-A coupling in prediction models have produced diagnostics that integrate across both the land and PBL components of the system. In this study, we examine the impact of improved specification of land surface states, anomalies, and fluxes on coupled WRF forecasts during the summers of extreme dry (2006) and wet (2007) land surface conditions in the U.S. Southern Great Plains. The improved land initialization and surface flux parameterizations are obtained through the use of a new optimization and uncertainty estimation module in NASA's Land Information System (US-OPT/UE), whereby parameter sets are calibrated in the Noah land surface model and classified according to a land cover and soil type mapping of the observation sites to the full model domain. The impact of calibrated parameters on the a) spinup of the land surface used as initial conditions, and b) heat and moisture states and fluxes of the coupled WRF Simulations are then assessed in terms of ambient weather and land-atmosphere coupling along with measures of uncertainty propagation into the forecasts. In addition, the sensitivity of this approach to the period of calibration (dry, wet, average) is investigated. Finally, tradeoffs of computational tractability and scientific validity, and the potential for combining this approach with satellite remote sensing data are also discussed.

Santanello, Joseph A.↗

NASA NTRS2012

Impact of Calibrated Land Surface Model Parameters on the Accuracy and Uncertainty of Land-Atmosphere Coupling in WRF Simulations

Land-atmosphere (L-A) interactions play a critical role in determining the diurnal evolution of both planetary boundary layer (PBL) and land surface temperature and moisture budgets, as well as controlling feedbacks with clouds and precipitation that lead to the persistence of dry and wet regimes. Recent efforts to quantify the strength of L-A coupling in prediction models have produced diagnostics that integrate across both the land and PBL components of the system. In this study, we examine the impact of improved specification of land surface states, anomalies, and fluxes on coupled WRF forecasts during the summers of extreme dry (2006) and wet (2007) land surface conditions in the U.S. Southern Great Plains. The improved land initialization and surface flux parameterizations are obtained through the use of a new optimization and uncertainty estimation module in NASA's Land Information System (LIS-OPT/UE), whereby parameter sets are calibrated in the Noah land surface model and classified according to a land cover and soil type mapping of the observation sites to the full model domain. The impact of calibrated parameters on the a) spinup of the land surface used as initial conditions, and b) heat and moisture states and fluxes of the coupled WRF simulations are then assessed in terms of ambient weather and land-atmosphere coupling along with measures of uncertainty propagation into the forecasts. In addition, the sensitivity of this approach to the period of calibration (dry, wet, average) is investigated. Finally, tradeoffs of computational tractability and scientific validity, and the potential for combining this approach with satellite remote sensing data are also discussed.

Santanello, Joseph A., Jr.↗

NASA NTRS2005

An Improved Analysis of Forest Carbon Dynamics using Data Assimilation

There are two broad approaches to quantifying landscape C dynamics - by measuring changes in C stocks over time, or by measuring fluxes of C directly. However, these data may be patchy, and have gaps or biases. An alternative approach to generating C budgets has been to use process-based models, constructed to simulate the key processes involved in C exchange. However, the process of model building is arguably subjective, and parameters may be poorly defined. This paper demonstrates why data assimilation (DA) techniques - which combine stock and flux observations with a dynamic model - improve estimates of, and provide insights into, ecosystem carbon (C) exchanges. We use an ensemble Kalman filter (EnKF) to link a series of measurements with a simple box model of C transformations. Measurements were collected at a young ponderosa pine stand in central Oregon over a 3-year period, and include eddy flux and soil C02 efflux data, litterfall collections, stem surveys, root and soil cores, and leaf area index data. The simple C model is a mass balance model with nine unknown parameters, tracking changes in C storage among five pools; foliar, wood and fine root pools in vegetation, and also fresh litter and soil organic matter (SOM) plus coarse woody debris pools. We nested the EnKF within an optimization routine to generate estimates from the data of the unknown parameters and the five initial conditions for the pools. The efficacy of the DA process can be judged by comparing the probability distributions of estimates produced with the EnKF analysis vs. those produced with reduced data or model alone. Using the model alone, estimated net ecosystem exchange of C (NEE)= -251 f 197g Cm-2 over the 3 years, compared with an estimate of -419 f 29gCm-2 when all observations were assimilated into the model. The uncertainty on daily measurements of NEE via eddy fluxes was estimated at 0.5gCm-2 day-1, but the uncertainty on assimilated estimates averaged 0.47 g Cm-2 day-1, and only exceeded 0.5gC m-2 day-1 on days where neither eddy flux nor soil efflux data were available. In generating C budgets, the assimilation process reduced the uncertainties associated with using data or model alone and the forecasts of NEE were statistically unbiased estimates. The results of the analysis emphasize the importance of time series as constraints. Occasional, rare measurements of stocks have limited use in constraining the estimates of other components of the C cycle. Long time series are particularly crucial for improving the analysis of pools with long time constants, such as SOM, woody biomass, and woody debris. Long-running forest stem surveys, and tree ring data, offer a rich resource that could be assimilated to provide an important constraint on C cycling of slow pools. For extending estimates of NEE across regions, DA can play a further important role, by assimilating remote-sensing data into the analysis of C cycles. We show, via sensitivity analysis, how assimilating an estimate of photosynthesis - which might be provided indirectly by remotely sensed data - improves the analysis of NEE.

Williams, Mathew↗

NASA NTRS2005

Enabling Pinpoint Landing (PPL) on Mars

Pinpoint landing (PPL) missions will deliver about 1000 kg of useful payload to the surface of Mars. Mid-to-high latitude landing site compatibility is sought which should provide the means to land at sites up to 2.5 km above Mars mean surface altitude. A dispersion and control analysis process is presented which helps to identify the effects of PPL error drivers, quantify the effect of dispersions on landing error and quantify the landing position control capability/authority along the entry path. An entry/descent/landing (EDL) profile is provided. Guided aeroshell is the baseline for all candidate Mars atmospheric entry architectures. A two-stage architecture is considered for the aerodynamic decelerator descent phase: supersonic parachute plus guided subsonic parachute or high-Mach inflatable decelerator plus guided subsonic parachute. The powered descent phase uses propulsive descent stage for soft landing and final error reduction maneuvers. Studies have found that the aeroshell entry face dispersions can be large, but closed-loop guidance can null out resulting errors to within about 2 km. Additionally, projected parachute control is inadequate to correct worst case dispersions without wind forecast data. To mitigate the problems dispersions due to atmospheric uncertainty can be reduced by providing on-board external means to measure density and winds ahead of the vehicle, higher L/D control authority options for the subsonic parachute phase can be investigated, and decelerators with control authority options for the supersonic descent phase can be examined. A navigation error analysis and wind effects summary are included.

aerodynamic↗

NASA NTRS2019

Uncertainty of Observation Impact Estimation in an Adjoint Model Investigated with an Observing System Simulation Experiment

Adjoint models are often used to estimate the impact of different observations on short-term forecast skill. A common difficulty with the evaluation of short term forecast quality is the choice of verification fields. The use of self-analysis fields for verification is typical but incestuous, and introduces uncertainty due to biases and errors in the analysis field. In this study, an observing system simulation experiment (OSSE) is used to explore the uncertainty in adjoint model estimations of observation impact. The availability of the true state for verification in the OSSE framework in the form of the Nature Run allows calculation of the observation impact without the uncertainties present in self-analysis verification. These impact estimates are compared to estimates calculated using self-analysis verification. The Global Earth Observing System version 5 (GEOS-5) forecast model with Gridpoint Statistical Interpolation (GSI) is used with the National Aeronautics and Space Administration Global Modeling and Assimilation Office (NASA/GMAO) OSSE capability. The adjoint model includes moist processes, with total wet energy selected as the norm for evaluation of observation impacts. The results show that there are measurable but small discrepancies in the adjoint model estimation of observation impact. In general, observations of temperature and winds tend to have overestimated impacts with self-analysis verification, while observations of humidity and moisture-affected observations tend to have underestimated impacts. The small magnitude of the differences in impact estimates supports the robustness of the adjoint method of estimating observation impacts.

N C Prive↗

NASA NTRS2017

Building Reliable Forecasts of Solar Activity

Solar ionizing radiation critically depends on the level of the Sun’s magnetic activity. For robust physics-based forecasts, we employ the procedure of data assimilation, which combines theoretical modeling and observational data such that uncertainties in both the model and the observations are taken into account. Currently we are working in two major directions: 1) development of a new long-term forecast procedure on time-scales of the 11-year solar cycle, using a 2-dimensional mean-field dynamo model and synoptic magnetograms; 2) development of 3-dimensional radiative MHD (Magnetohydrodynamic) simulations to investigate the origin and precursors of local manifestations of magnetic activity, such as the formation of magnetic structures and eruptive dynamics.

Kitiashvili, Irina↗

NASA NTRS2012

Diagnosing the Nature of Land-Atmosphere Coupling: A Case Study of Dry/Wet Extremes

Land-atmosphere (L-A) interactions play a critical role in determining the diurnal evolution of land surface and planetary boundary layer (PBL) temperature and moisture states and fluxes. In turn, these interactions regulate the strength of the connection between surface moisture and precipitation in a coupled system. To address deficiencies in numerical weather prediction and climate models due to improper treatment of L-A interactions, recent studies have focused on development of diagnostics to quantify the strength and accuracy of the land-PBL coupling at the process-level. In this study, a diagnosis of the nature and impacts oflocalland-atmosphere coupling (LoCo) during dry and wet extreme conditions is presented using a combination of models and observations during the summers of2006-7 in the U.S. Southern Great Plains. Specifically, the Weather Research and Forecasting (WRF) model has been coupled to NASA's Land Information System (LIS), which provides a flexible and high-resolution representation and initialization of land surface physics and states. A range of diagnostics exploring the links and feedbacks between soil moisture and precipitation are examined for the dry/wet regimes of this region, along with the behavior and accuracy of different land-PBL scheme couplings under these conditions. In addition, we examine the impact of improved specification ofland surface states, anomalies, and fluxes that are obtained through the use of a hew optimization and uncertainty module in LIS, on the L-A coupling in WRF forecasts. Results demonstrate how LoCo diagnostics can be applied to coupled model components in the context of their integrated impacts on the process-chain connecting the land surface to the PBL and support of hydrological anomalies.

Santanello, Joseph A., Jr.↗

NASA NTRS2011

Incorporating JULES into NASA's Land Information System (LIS) and Investigations of Land-Atmosphere Coupling

NASA's Land Information System (LIS; lis.gsfc.nasa.gov) is a flexible land surface modeling and data assimilation framework developed over the past decade with the goal of integrating satellite- and ground-based observational data products and advanced land surface modeling techniques to produce optimal fields of land surface states and fluxes. LIS features a high performance and flexible design, and operates on an ensemble of land surface models for extension over user-specified regional or global domains. The extensible interfaces of LIS allow the incorporation of new domains, land surface models (LSMs), land surface parameters, meteorological inputs, data assimilation and optimization algorithms. In addition, LIS has also been demonstrated for parameter estimation and uncertainty estimation, and has been coupled to the Weather Research and Forecasting (WRF) mesoscale model. A visiting fellowship is currently underway to implement JULES into LIS and to undertake some fundamental science on the feedbacks between the land surface and the atmosphere. An overview of the LIS system, features, and sample results will be presented in an effort to engage the community in the potential advantages of LIS-JULES for a range of applications. Ongoing efforts to develop a framework for diagnosing land-atmosphere coupling will also be presented using the suite of LSM and PBL schemes available in LIS and WRF along with observations from the U. S .. Southern Great Plains. This methodology provides a potential pathway to study factors controlling local land-atmosphere coupling (LoCo) using the LIS-WRF system, which will serve as a testbed for future experiments to evaluate coupling diagnostics within the community.

Santanello, Joseph↗

NASA NTRS2010

A Modeling and Verification Study of Summer Precipitation Systems Using NASA Surface Initialization Datasets

One of the most challenging weather forecast problems in the southeastern U.S. is daily summertime pulse-type convection. During the summer, atmospheric flow and forcing are generally weak in this region; thus, convection typically initiates in response to local forcing along sea/lake breezes, and other discontinuities often related to horizontal gradients in surface heating rates. Numerical simulations of pulse convection usually have low skill, even in local predictions at high resolution, due to the inherent chaotic nature of these precipitation systems. Forecast errors can arise from assumptions within parameterization schemes, model resolution limitations, and uncertainties in both the initial state of the atmosphere and land surface variables such as soil moisture and temperature. For this study, it is hypothesized that high-resolution, consistent representations of surface properties such as soil moisture, soil temperature, and sea surface temperature (SST) are necessary to better simulate the interactions between the surface and atmosphere, and ultimately improve predictions of summertime pulse convection. This paper describes a sensitivity experiment using the Weather Research and Forecasting (WRF) model. Interpolated land and ocean surface fields from a large-scale model are replaced with high-resolution datasets provided by unique NASA assets in an experimental simulation: the Land Information System (LIS) and Moderate Resolution Imaging Spectroradiometer (MODIS) SSTs. The LIS is run in an offline mode for several years at the same grid resolution as the WRF model to provide compatible land surface initial conditions in an equilibrium state. The MODIS SSTs provide detailed analyses of SSTs over the oceans and large lakes compared to current operational products. The WRF model runs initialized with the LIS+MODIS datasets result in a reduction in the overprediction of rainfall areas; however, the skill is almost equally as low in both experiments using traditional verification methodologies. Output from object-based verification within NCAR s Meteorological Evaluation Tools reveals that the WRF runs initialized with LIS+MODIS data consistently generated precipitation objects that better matched observed precipitation objects, especially at higher precipitation intensities. The LIS+MODIS runs produced on average a 4% increase in matched precipitation areas and a simultaneous 4% decrease in unmatched areas during three months of daily simulations.

Jonathan L. Case↗

NASA NTRS2018

Synergy of Observations and Dynamo Models to Understand and Predict Solar Activity Cycles

The long-standing problem of understanding the evolution of the global magnetic fields that drive solar activity through different temporal scales is becoming more tractable because, in addition to 400 years of sunspot records, we now have almost 4 solar cycles of magnetic field observations. These observations allow us to discern physical connections between dynamo model variables and observations using data assimilation analysis. In particular, the Ensemble Kalman Filter approach takes into account uncertainties in both observations and modeling and allows us to make reliable forecasts of solar cycle activity cycles by using a relatively simple non-linear dynamical model of the solar dynamo. To expand this approach for more complex 2D and 3D dynamo modeling, it is necessary to decompose the observed synoptic magnetograms into poloidal and toroidal field components. In this presentation I will present initial results on magnetogram decomposition and assimilation of magnetogram data into dynamo modeling.

Synergy↗

NASA NTRS2018

Synergy of Observations and Dynamo Models to Understand and Predict Solar Activity Cycles

The long-standing problem of understanding the evolution of the global magnetic fields that drive solar activity through different temporal scales is becoming more tractable because, in addition to 400 years of sunspot records, we now have almost 4 solar cycles of magnetic field observations. These observations allow us to discern physical connections between dynamo model variables and observations using data assimilation analysis. In particular, the Ensemble Kalman Filter approach takes into account uncertainties in both observations and modeling and allows us to make reliable forecasts of solar cycle activity by using a relatively simple non-linear dynamical model of the solar dynamo. To expand this approach for more complex 2D and 3D dynamo modeling, it is necessary to decompose the observed synoptic magnetograms into poloidal and toroidal field components. In this presentation I will present initial results on magnetogram decomposition and assimilation of magnetogram data into dynamo modeling.

Kitiashvili, Irina N.↗

NASA NTRSDate not supplied

TPSAS-NF1676L-13135-DND

Aircraft Icing - Aircraft structures act as ice nuclei in supercooled clouds - ice collects, weight increases, plane falls - Pilots need to know where and when icing can occur - PIREPS are first order: sparse, aircraft dependent, location uncertain - Model analyses and forecasts: freezing levels, cloud expectations - radar => precipitation - All combined in NCAR/FAA/NOAA/NASA program to provide Current Icing Product (CIP) & Forecast Icing product (FIP) analyses to pilots (CONUS) - some inadequacies remain - NWP uncertainties, intensity, altitude of icing, etc. - Operational satellites can add valuable information - indirectly: input of cloud properties to CIP & NWP - directly: determination of icing threat from real time satellite data

William L Smith↗

NASA NTRSDate not supplied

Investigation of Coupled Land-Atmosphere Carbon Dynamics and Seasonal Carbon Forecast Skill

In this talk, I will present recent efforts to explore coupled land-atmosphere carbon dynamics and carbon cycle predictability. First, the impact of a regional drought on land and atmospheric carbon was studied by imposing an idealized spring drought in coupled land‐atmosphere ensemble simulations. Through drought-induced impact on remote meteorology, the drought alters land’s productivity not only in the drought area but also in the adjacent areas. The atmospheric CO2 anomalies extend to an area up to three times of that of the imposed drought, which suggests that atmospheric transport needs to be considered in the interpretation of drought‐induced carbon anomalies. Increase in column‐averaged monthly CO2 by the imposed drought is at the edge of the uncertainty from single soundings of current greenhouse gas observing satellites. Secondly, the seasonal carbon forecast skill was explored using NASA’s subseasonal-to-seasonal (S2S) ensemble forecast and a terrestrial biosphere model. The result demonstrates an ability to accurately predict spring-summer carbon uptake at multi-month leads in the Northern Hemisphere mid- and high latitude land. The skill appears to be achieved by accurate forecasts of snow removal timing as well as proper initialization of carbon and nitrogen states. Additionally, I will briefly discuss other ongoing research activities to improve hydrometeorological prediction by integrating biogeochemical processes, to evaluate forecast skill of wildfire, and to apply subseasonal forecasts to water resources management.

Eunjee Lee↗